research essay about bullying

• Research article
• Open access
• Published: 14 December 2021

Bullying at school and mental health problems among adolescents: a repeated cross-sectional study

• Håkan Källmén 1 &
• Mats Hallgren   ORCID: orcid.org/0000-0002-0599-2403 2  

Child and Adolescent Psychiatry and Mental Health volume  15 , Article number:  74 ( 2021 ) Cite this article

105k Accesses

16 Citations

37 Altmetric

Metrics details

To examine recent trends in bullying and mental health problems among adolescents and the association between them.

A questionnaire measuring mental health problems, bullying at school, socio-economic status, and the school environment was distributed to all secondary school students aged 15 (school-year 9) and 18 (school-year 11) in Stockholm during 2014, 2018, and 2020 (n = 32,722). Associations between bullying and mental health problems were assessed using logistic regression analyses adjusting for relevant demographic, socio-economic, and school-related factors.

The prevalence of bullying remained stable and was highest among girls in year 9; range = 4.9% to 16.9%. Mental health problems increased; range = + 1.2% (year 9 boys) to + 4.6% (year 11 girls) and were consistently higher among girls (17.2% in year 11, 2020). In adjusted models, having been bullied was detrimentally associated with mental health (OR = 2.57 [2.24–2.96]). Reports of mental health problems were four times higher among boys who had been bullied compared to those not bullied. The corresponding figure for girls was 2.4 times higher.

Conclusions

Exposure to bullying at school was associated with higher odds of mental health problems. Boys appear to be more vulnerable to the deleterious effects of bullying than girls.

Introduction

Bullying involves repeated hurtful actions between peers where an imbalance of power exists [ 1 ]. Arseneault et al. [ 2 ] conducted a review of the mental health consequences of bullying for children and adolescents and found that bullying is associated with severe symptoms of mental health problems, including self-harm and suicidality. Bullying was shown to have detrimental effects that persist into late adolescence and contribute independently to mental health problems. Updated reviews have presented evidence indicating that bullying is causative of mental illness in many adolescents [ 3 , 4 ].

There are indications that mental health problems are increasing among adolescents in some Nordic countries. Hagquist et al. [ 5 ] examined trends in mental health among Scandinavian adolescents (n = 116, 531) aged 11–15 years between 1993 and 2014. Mental health problems were operationalized as difficulty concentrating, sleep disorders, headache, stomach pain, feeling tense, sad and/or dizzy. The study revealed increasing rates of adolescent mental health problems in all four counties (Finland, Sweden, Norway, and Denmark), with Sweden experiencing the sharpest increase among older adolescents, particularly girls. Worsening adolescent mental health has also been reported in the United Kingdom. A study of 28,100 school-aged adolescents in England found that two out of five young people scored above thresholds for emotional problems, conduct problems or hyperactivity [ 6 ]. Female gender, deprivation, high needs status (educational/social), ethnic background, and older age were all associated with higher odds of experiencing mental health difficulties.

Bullying is shown to increase the risk of poor mental health and may partly explain these detrimental changes. Le et al. [ 7 ] reported an inverse association between bullying and mental health among 11–16-year-olds in Vietnam. They also found that poor mental health can make some children and adolescents more vulnerable to bullying at school. Bayer et al. [ 8 ] examined links between bullying at school and mental health among 8–9-year-old children in Australia. Those who experienced bullying more than once a week had poorer mental health than children who experienced bullying less frequently. Friendships moderated this association, such that children with more friends experienced fewer mental health problems (protective effect). Hysing et al. [ 9 ] investigated the association between experiences of bullying (as a victim or perpetrator) and mental health, sleep disorders, and school performance among 16–19 year olds from Norway (n = 10,200). Participants were categorized as victims, bullies, or bully-victims (that is, victims who also bullied others). All three categories were associated with worse mental health, school performance, and sleeping difficulties. Those who had been bullied also reported more emotional problems, while those who bullied others reported more conduct disorders [ 9 ].

As most adolescents spend a considerable amount of time at school, the school environment has been a major focus of mental health research [ 10 , 11 ]. In a recent review, Saminathen et al. [ 12 ] concluded that school is a potential protective factor against mental health problems, as it provides a socially supportive context and prepares students for higher education and employment. However, it may also be the primary setting for protracted bullying and stress [ 13 ]. Another factor associated with adolescent mental health is parental socio-economic status (SES) [ 14 ]. A systematic review indicated that lower parental SES is associated with poorer adolescent mental health [ 15 ]. However, no previous studies have examined whether SES modifies or attenuates the association between bullying and mental health. Similarly, it remains unclear whether school related factors, such as school grades and the school environment, influence the relationship between bullying and mental health. This information could help to identify those adolescents most at risk of harm from bullying.

To address these issues, we investigated the prevalence of bullying at school and mental health problems among Swedish adolescents aged 15–18 years between 2014 and 2020 using a population-based school survey. We also examined associations between bullying at school and mental health problems adjusting for relevant demographic, socioeconomic, and school-related factors. We hypothesized that: (1) bullying and adolescent mental health problems have increased over time; (2) There is an association between bullying victimization and mental health, so that mental health problems are more prevalent among those who have been victims of bullying; and (3) that school-related factors would attenuate the association between bullying and mental health.

Participants

The Stockholm school survey is completed every other year by students in lower secondary school (year 9—compulsory) and upper secondary school (year 11). The survey is mandatory for public schools, but voluntary for private schools. The purpose of the survey is to help inform decision making by local authorities that will ultimately improve students’ wellbeing. The questions relate to life circumstances, including SES, schoolwork, bullying, drug use, health, and crime. Non-completers are those who were absent from school when the survey was completed (< 5%). Response rates vary from year to year but are typically around 75%. For the current study data were available for 2014, 2018 and 2020. In 2014; 5235 boys and 5761 girls responded, in 2018; 5017 boys and 5211 girls responded, and in 2020; 5633 boys and 5865 girls responded (total n = 32,722). Data for the exposure variable, bullied at school, were missing for 4159 students, leaving 28,563 participants in the crude model. The fully adjusted model (described below) included 15,985 participants. The mean age in grade 9 was 15.3 years (SD = 0.51) and in grade 11, 17.3 years (SD = 0.61). As the data are completely anonymous, the study was exempt from ethical approval according to an earlier decision from the Ethical Review Board in Stockholm (2010-241 31-5). Details of the survey are available via a website [ 16 ], and are described in a previous paper [ 17 ].

Students completed the questionnaire during a school lesson, placed it in a sealed envelope and handed it to their teacher. Student were permitted the entire lesson (about 40 min) to complete the questionnaire and were informed that participation was voluntary (and that they were free to cancel their participation at any time without consequences). Students were also informed that the Origo Group was responsible for collection of the data on behalf of the City of Stockholm.

Study outcome

Mental health problems were assessed by using a modified version of the Psychosomatic Problem Scale [ 18 ] shown to be appropriate for children and adolescents and invariant across gender and years. The scale was later modified [ 19 ]. In the modified version, items about difficulty concentrating and feeling giddy were deleted and an item about ‘life being great to live’ was added. Seven different symptoms or problems, such as headaches, depression, feeling fear, stomach problems, difficulty sleeping, believing it’s great to live (coded negatively as seldom or rarely) and poor appetite were used. Students who responded (on a 5-point scale) that any of these problems typically occurs ‘at least once a week’ were considered as having indicators of a mental health problem. Cronbach alpha was 0.69 across the whole sample. Adding these problem areas, a total index was created from 0 to 7 mental health symptoms. Those who scored between 0 and 4 points on the total symptoms index were considered to have a low indication of mental health problems (coded as 0); those who scored between 5 and 7 symptoms were considered as likely having mental health problems (coded as 1).

Primary exposure

Experiences of bullying were measured by the following two questions: Have you felt bullied or harassed during the past school year? Have you been involved in bullying or harassing other students during this school year? Alternatives for the first question were: yes or no with several options describing how the bullying had taken place (if yes). Alternatives indicating emotional bullying were feelings of being mocked, ridiculed, socially excluded, or teased. Alternatives indicating physical bullying were being beaten, kicked, forced to do something against their will, robbed, or locked away somewhere. The response alternatives for the second question gave an estimation of how often the respondent had participated in bullying others (from once to several times a week). Combining the answers to these two questions, five different categories of bullying were identified: (1) never been bullied and never bully others; (2) victims of emotional (verbal) bullying who have never bullied others; (3) victims of physical bullying who have never bullied others; (4) victims of bullying who have also bullied others; and (5) perpetrators of bullying, but not victims. As the number of positive cases in the last three categories was low (range = 3–15 cases) bully categories 2–4 were combined into one primary exposure variable: ‘bullied at school’.

Assessment year was operationalized as the year when data was collected: 2014, 2018, and 2020. Age was operationalized as school grade 9 (15–16 years) or 11 (17–18 years). Gender was self-reported (boy or girl). The school situation To assess experiences of the school situation, students responded to 18 statements about well-being in school, participation in important school matters, perceptions of their teachers, and teaching quality. Responses were given on a four-point Likert scale ranging from ‘do not agree at all’ to ‘fully agree’. To reduce the 18-items down to their essential factors, we performed a principal axis factor analysis. Results showed that the 18 statements formed five factors which, according to the Kaiser criterion (eigen values > 1) explained 56% of the covariance in the student’s experience of the school situation. The five factors identified were: (1) Participation in school; (2) Interesting and meaningful work; (3) Feeling well at school; (4) Structured school lessons; and (5) Praise for achievements. For each factor, an index was created that was dichotomised (poor versus good circumstance) using the median-split and dummy coded with ‘good circumstance’ as reference. A description of the items included in each factor is available as Additional file 1 . Socio-economic status (SES) was assessed with three questions about the education level of the student’s mother and father (dichotomized as university degree versus not), and the amount of spending money the student typically received for entertainment each month (> SEK 1000 [approximately $120] versus less). Higher parental education and more spending money were used as reference categories. School grades in Swedish, English, and mathematics were measured separately on a 7-point scale and dichotomized as high (grades A, B, and C) versus low (grades D, E, and F). High school grades were used as the reference category.

Statistical analyses

The prevalence of mental health problems and bullying at school are presented using descriptive statistics, stratified by survey year (2014, 2018, 2020), gender, and school year (9 versus 11). As noted, we reduced the 18-item questionnaire assessing school function down to five essential factors by conducting a principal axis factor analysis (see Additional file 1 ). We then calculated the association between bullying at school (defined above) and mental health problems using multivariable logistic regression. Results are presented as odds ratios (OR) with 95% confidence intervals (Cis). To assess the contribution of SES and school-related factors to this association, three models are presented: Crude, Model 1 adjusted for demographic factors: age, gender, and assessment year; Model 2 adjusted for Model 1 plus SES (parental education and student spending money), and Model 3 adjusted for Model 2 plus school-related factors (school grades and the five factors identified in the principal factor analysis). These covariates were entered into the regression models in three blocks, where the final model represents the fully adjusted analyses. In all models, the category ‘not bullied at school’ was used as the reference. Pseudo R-square was calculated to estimate what proportion of the variance in mental health problems was explained by each model. Unlike the R-square statistic derived from linear regression, the Pseudo R-square statistic derived from logistic regression gives an indicator of the explained variance, as opposed to an exact estimate, and is considered informative in identifying the relative contribution of each model to the outcome [ 20 ]. All analyses were performed using SPSS v. 26.0.

Prevalence of bullying at school and mental health problems

Estimates of the prevalence of bullying at school and mental health problems across the 12 strata of data (3 years × 2 school grades × 2 genders) are shown in Table 1 . The prevalence of bullying at school increased minimally (< 1%) between 2014 and 2020, except among girls in grade 11 (2.5% increase). Mental health problems increased between 2014 and 2020 (range = 1.2% [boys in year 11] to 4.6% [girls in year 11]); were three to four times more prevalent among girls (range = 11.6% to 17.2%) compared to boys (range = 2.6% to 4.9%); and were more prevalent among older adolescents compared to younger adolescents (range = 1% to 3.1% higher). Pooling all data, reports of mental health problems were four times more prevalent among boys who had been victims of bullying compared to those who reported no experiences with bullying. The corresponding figure for girls was two and a half times as prevalent.

Associations between bullying at school and mental health problems

Table 2 shows the association between bullying at school and mental health problems after adjustment for relevant covariates. Demographic factors, including female gender (OR = 3.87; CI 3.48–4.29), older age (OR = 1.38, CI 1.26–1.50), and more recent assessment year (OR = 1.18, CI 1.13–1.25) were associated with higher odds of mental health problems. In Model 2, none of the included SES variables (parental education and student spending money) were associated with mental health problems. In Model 3 (fully adjusted), the following school-related factors were associated with higher odds of mental health problems: lower grades in Swedish (OR = 1.42, CI 1.22–1.67); uninteresting or meaningless schoolwork (OR = 2.44, CI 2.13–2.78); feeling unwell at school (OR = 1.64, CI 1.34–1.85); unstructured school lessons (OR = 1.31, CI = 1.16–1.47); and no praise for achievements (OR = 1.19, CI 1.06–1.34). After adjustment for all covariates, being bullied at school remained associated with higher odds of mental health problems (OR = 2.57; CI 2.24–2.96). Demographic and school-related factors explained 12% and 6% of the variance in mental health problems, respectively (Pseudo R-Square). The inclusion of socioeconomic factors did not alter the variance explained.

Our findings indicate that mental health problems increased among Swedish adolescents between 2014 and 2020, while the prevalence of bullying at school remained stable (< 1% increase), except among girls in year 11, where the prevalence increased by 2.5%. As previously reported [ 5 , 6 ], mental health problems were more common among girls and older adolescents. These findings align with previous studies showing that adolescents who are bullied at school are more likely to experience mental health problems compared to those who are not bullied [ 3 , 4 , 9 ]. This detrimental relationship was observed after adjustment for school-related factors shown to be associated with adolescent mental health [ 10 ].

A novel finding was that boys who had been bullied at school reported a four-times higher prevalence of mental health problems compared to non-bullied boys. The corresponding figure for girls was 2.5 times higher for those who were bullied compared to non-bullied girls, which could indicate that boys are more vulnerable to the deleterious effects of bullying than girls. Alternatively, it may indicate that boys are (on average) bullied more frequently or more intensely than girls, leading to worse mental health. Social support could also play a role; adolescent girls often have stronger social networks than boys and could be more inclined to voice concerns about bullying to significant others, who in turn may offer supports which are protective [ 21 ]. Related studies partly confirm this speculative explanation. An Estonian study involving 2048 children and adolescents aged 10–16 years found that, compared to girls, boys who had been bullied were more likely to report severe distress, measured by poor mental health and feelings of hopelessness [ 22 ].

Other studies suggest that heritable traits, such as the tendency to internalize problems and having low self-esteem are associated with being a bully-victim [ 23 ]. Genetics are understood to explain a large proportion of bullying-related behaviors among adolescents. A study from the Netherlands involving 8215 primary school children found that genetics explained approximately 65% of the risk of being a bully-victim [ 24 ]. This proportion was similar for boys and girls. Higher than average body mass index (BMI) is another recognized risk factor [ 25 ]. A recent Australian trial involving 13 schools and 1087 students (mean age = 13 years) targeted adolescents with high-risk personality traits (hopelessness, anxiety sensitivity, impulsivity, sensation seeking) to reduce bullying at school; both as victims and perpetrators [ 26 ]. There was no significant intervention effect for bullying victimization or perpetration in the total sample. In a secondary analysis, compared to the control schools, intervention school students showed greater reductions in victimization, suicidal ideation, and emotional symptoms. These findings potentially support targeting high-risk personality traits in bullying prevention [ 26 ].

The relative stability of bullying at school between 2014 and 2020 suggests that other factors may better explain the increase in mental health problems seen here. Many factors could be contributing to these changes, including the increasingly competitive labour market, higher demands for education, and the rapid expansion of social media [ 19 , 27 , 28 ]. A recent Swedish study involving 29,199 students aged between 11 and 16 years found that the effects of school stress on psychosomatic symptoms have become stronger over time (1993–2017) and have increased more among girls than among boys [ 10 ]. Research is needed examining possible gender differences in perceived school stress and how these differences moderate associations between bullying and mental health.

Strengths and limitations

Strengths of the current study include the large participant sample from diverse schools; public and private, theoretical and practical orientations. The survey included items measuring diverse aspects of the school environment; factors previously linked to adolescent mental health but rarely included as covariates in studies of bullying and mental health. Some limitations are also acknowledged. These data are cross-sectional which means that the direction of the associations cannot be determined. Moreover, all the variables measured were self-reported. Previous studies indicate that students tend to under-report bullying and mental health problems [ 29 ]; thus, our results may underestimate the prevalence of these behaviors.

In conclusion, consistent with our stated hypotheses, we observed an increase in self-reported mental health problems among Swedish adolescents, and a detrimental association between bullying at school and mental health problems. Although bullying at school does not appear to be the primary explanation for these changes, bullying was detrimentally associated with mental health after adjustment for relevant demographic, socio-economic, and school-related factors, confirming our third hypothesis. The finding that boys are potentially more vulnerable than girls to the deleterious effects of bullying should be replicated in future studies, and the mechanisms investigated. Future studies should examine the longitudinal association between bullying and mental health, including which factors mediate/moderate this relationship. Epigenetic studies are also required to better understand the complex interaction between environmental and biological risk factors for adolescent mental health [ 24 ].

Availability of data and materials

Data requests will be considered on a case-by-case basis; please email the corresponding author.

Code availability

Not applicable.

Olweus D. School bullying: development and some important challenges. Ann Rev Clin Psychol. 2013;9(9):751–80. https://doi.org/10.1146/annurev-clinpsy-050212-185516 .

Article   Google Scholar  

Arseneault L, Bowes L, Shakoor S. Bullying victimization in youths and mental health problems: “Much ado about nothing”? Psychol Med. 2010;40(5):717–29. https://doi.org/10.1017/S0033291709991383 .

Article   CAS   PubMed   Google Scholar  

Arseneault L. The long-term impact of bullying victimization on mental health. World Psychiatry. 2017;16(1):27–8. https://doi.org/10.1002/wps.20399 .

Article   PubMed   PubMed Central   Google Scholar  

Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: a systematic review and meta-analysis. World J Psychiatry. 2017;7(1):60–76. https://doi.org/10.5498/wjp.v7.i1.60 .

Hagquist C, Due P, Torsheim T, Valimaa R. Cross-country comparisons of trends in adolescent psychosomatic symptoms—a Rasch analysis of HBSC data from four Nordic countries. Health Qual Life Outcomes. 2019;17(1):27. https://doi.org/10.1186/s12955-019-1097-x .

Deighton J, Lereya ST, Casey P, Patalay P, Humphrey N, Wolpert M. Prevalence of mental health problems in schools: poverty and other risk factors among 28 000 adolescents in England. Br J Psychiatry. 2019;215(3):565–7. https://doi.org/10.1192/bjp.2019.19 .

Article   PubMed Central   Google Scholar  

Le HTH, Tran N, Campbell MA, Gatton ML, Nguyen HT, Dunne MP. Mental health problems both precede and follow bullying among adolescents and the effects differ by gender: a cross-lagged panel analysis of school-based longitudinal data in Vietnam. Int J Ment Health Syst. 2019. https://doi.org/10.1186/s13033-019-0291-x .

Bayer JK, Mundy L, Stokes I, Hearps S, Allen N, Patton G. Bullying, mental health and friendship in Australian primary school children. Child Adolesc Ment Health. 2018;23(4):334–40. https://doi.org/10.1111/camh.12261 .

Article   PubMed   Google Scholar  

Hysing M, Askeland KG, La Greca AM, Solberg ME, Breivik K, Sivertsen B. Bullying involvement in adolescence: implications for sleep, mental health, and academic outcomes. J Interpers Violence. 2019. https://doi.org/10.1177/0886260519853409 .

Hogberg B, Strandh M, Hagquist C. Gender and secular trends in adolescent mental health over 24 years—the role of school-related stress. Soc Sci Med. 2020. https://doi.org/10.1016/j.socscimed.2020.112890 .

Kidger J, Araya R, Donovan J, Gunnell D. The effect of the school environment on the emotional health of adolescents: a systematic review. Pediatrics. 2012;129(5):925–49. https://doi.org/10.1542/peds.2011-2248 .

Saminathen MG, Låftman SB, Modin B. En fungerande skola för alla: skolmiljön som skyddsfaktor för ungas psykiska välbefinnande. [A functioning school for all: the school environment as a protective factor for young people’s mental well-being]. Socialmedicinsk tidskrift [Soc Med]. 2020;97(5–6):804–16.

Google Scholar  

Bibou-Nakou I, Tsiantis J, Assimopoulos H, Chatzilambou P, Giannakopoulou D. School factors related to bullying: a qualitative study of early adolescent students. Soc Psychol Educ. 2012;15(2):125–45. https://doi.org/10.1007/s11218-012-9179-1 .

Vukojevic M, Zovko A, Talic I, Tanovic M, Resic B, Vrdoljak I, Splavski B. Parental socioeconomic status as a predictor of physical and mental health outcomes in children—literature review. Acta Clin Croat. 2017;56(4):742–8. https://doi.org/10.20471/acc.2017.56.04.23 .

Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: a systematic review. Soc Sci Med. 2013;90:24–31. https://doi.org/10.1016/j.socscimed.2013.04.026 .

Stockholm City. Stockholmsenkät (The Stockholm Student Survey). 2021. https://start.stockholm/aktuellt/nyheter/2020/09/presstraff-stockholmsenkaten-2020/ . Accessed 19 Nov 2021.

Zeebari Z, Lundin A, Dickman PW, Hallgren M. Are changes in alcohol consumption among swedish youth really occurring “in concert”? A new perspective using quantile regression. Alc Alcohol. 2017;52(4):487–95. https://doi.org/10.1093/alcalc/agx020 .

Hagquist C. Psychometric properties of the PsychoSomatic Problems Scale: a Rasch analysis on adolescent data. Social Indicat Res. 2008;86(3):511–23. https://doi.org/10.1007/s11205-007-9186-3 .

Hagquist C. Ungas psykiska hälsa i Sverige–komplexa trender och stora kunskapsluckor [Young people’s mental health in Sweden—complex trends and large knowledge gaps]. Socialmedicinsk tidskrift [Soc Med]. 2013;90(5):671–83.

Wu W, West SG. Detecting misspecification in mean structures for growth curve models: performance of pseudo R(2)s and concordance correlation coefficients. Struct Equ Model. 2013;20(3):455–78. https://doi.org/10.1080/10705511.2013.797829 .

Holt MK, Espelage DL. Perceived social support among bullies, victims, and bully-victims. J Youth Adolscence. 2007;36(8):984–94. https://doi.org/10.1007/s10964-006-9153-3 .

Mark L, Varnik A, Sisask M. Who suffers most from being involved in bullying-bully, victim, or bully-victim? J Sch Health. 2019;89(2):136–44. https://doi.org/10.1111/josh.12720 .

Tsaousis I. The relationship of self-esteem to bullying perpetration and peer victimization among schoolchildren and adolescents: a meta-analytic review. Aggress Violent Behav. 2016;31:186–99. https://doi.org/10.1016/j.avb.2016.09.005 .

Veldkamp SAM, Boomsma DI, de Zeeuw EL, van Beijsterveldt CEM, Bartels M, Dolan CV, van Bergen E. Genetic and environmental influences on different forms of bullying perpetration, bullying victimization, and their co-occurrence. Behav Genet. 2019;49(5):432–43. https://doi.org/10.1007/s10519-019-09968-5 .

Janssen I, Craig WM, Boyce WF, Pickett W. Associations between overweight and obesity with bullying behaviors in school-aged children. Pediatrics. 2004;113(5):1187–94. https://doi.org/10.1542/peds.113.5.1187 .

Kelly EV, Newton NC, Stapinski LA, Conrod PJ, Barrett EL, Champion KE, Teesson M. A novel approach to tackling bullying in schools: personality-targeted intervention for adolescent victims and bullies in Australia. J Am Acad Child Adolesc Psychiatry. 2020;59(4):508. https://doi.org/10.1016/j.jaac.2019.04.010 .

Gunnell D, Kidger J, Elvidge H. Adolescent mental health in crisis. BMJ. 2018. https://doi.org/10.1136/bmj.k2608 .

O’Reilly M, Dogra N, Whiteman N, Hughes J, Eruyar S, Reilly P. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin Child Psychol Psychiatry. 2018;23:601–13.

Unnever JD, Cornell DG. Middle school victims of bullying: who reports being bullied? Aggr Behav. 2004;30(5):373–88. https://doi.org/10.1002/ab.20030 .

Download references

Acknowledgements

Authors are grateful to the Department for Social Affairs, Stockholm, for permission to use data from the Stockholm School Survey.

Open access funding provided by Karolinska Institute. None to declare.

Author information

Authors and affiliations.

Stockholm Prevents Alcohol and Drug Problems (STAD), Center for Addiction Research and Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden

Håkan Källmén

Epidemiology of Psychiatric Conditions, Substance Use and Social Environment (EPiCSS), Department of Global Public Health, Karolinska Institutet, Level 6, Solnavägen 1e, Solna, Sweden

Mats Hallgren

You can also search for this author in PubMed   Google Scholar

Contributions

HK conceived the study and analyzed the data (with input from MH). HK and MH interpreted the data and jointly wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mats Hallgren .

Ethics declarations

Ethics approval and consent to participate.

As the data are completely anonymous, the study was exempt from ethical approval according to an earlier decision from the Ethical Review Board in Stockholm (2010-241 31-5).

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

Principal factor analysis description.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Källmén, H., Hallgren, M. Bullying at school and mental health problems among adolescents: a repeated cross-sectional study. Child Adolesc Psychiatry Ment Health 15 , 74 (2021). https://doi.org/10.1186/s13034-021-00425-y

Download citation

Received : 05 October 2021

Accepted : 23 November 2021

Published : 14 December 2021

DOI : https://doi.org/10.1186/s13034-021-00425-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Mental health
• Adolescents
• School-related factors
• Gender differences

Child and Adolescent Psychiatry and Mental Health

ISSN: 1753-2000

• Submission enquiries: [email protected]

Preventing Bullying: Consequences, Prevention, and Intervention

• Suzanne Le Menestrel National Academies of Sciences, Engineering, and Medicine

Bullying is considered to be a significant public health problem with both short- and long-term physical and social-emotional consequences for youth. A large body of research indicates that youth who have been bullied are at increased risk of subsequent mental, emotional, health, and behavioral problems, especially internalizing problems, such as low self-esteem, depression, anxiety, and loneliness. Given the growing awareness of bullying as a public health problem and the increasing evidence of short- and long-term physical, mental, emotional, and behavioral health and academic consequences of bullying behavior, there have been significant efforts at the practice, program, and policy levels to address bullying behavior. This article summarizes a recent consensus report from the National Academies of Sciences, Engineering, and Medicine, Preventing Bullying Through Science, Policy, and Practice , and what is known about the consequences of bullying behavior and interventions that attempt to prevent and respond to it.

Author Biography

Suzanne le menestrel, national academies of sciences, engineering, and medicine.

American Psychological Association Zero Tolerance Task Force. (2008). Are zero tolerance policies effective in the schools?: An evidentiary review and recommendations. American Psychologist, 63(9) 852-862. https://doi.org/10.1037/0003-066X.63.9.852

Beran, T. (2009). Correlates of peer victimization and achievement: An exploratory model. Psychology in the Schools, 46(4), 348-361. https://doi.org/10.1002/pits.20380

Beran, T. N., Hughes, G., & Lupart, J. (2008). A model of achievement and bullying: Analyses of the Canadian National Longitudinal Survey of Children and Youth data. Educational Research, 50(1), 25-39. https://doi.org/10.1080/00131880801920379

Beran, T. N., & Lupart, J. (2009). The relationship between school achievement and peer harassment in Canadian adolescents: The importance of mediating factors. School Psychology International, 30(1), 75-91. https://doi.org/10.1177/0143034308101851

Boccanfuso, C., & Kuhfeld, M. (2011, March). Multiple responses, promising results: Evidence-based nonpunitive alternatives to zero tolerance (Research to Results Brief, Child Trends Publication No. 2011-09). http://www.childtrends.org/wp-content/uploads/2011/03/Child_Trends-2011_03_01_RB_AltToZeroTolerance.pdf

Bradshaw, C. P. (2015). Translating research to practice in bullying prevention. American Psychologist, 70(4), 322. https://doi.org/10.1037/a0039114

de Bruyn, E., Cillessen, A., & Wissink, I. (2010). Associations of peer acceptance and perceived popularity with bullying and victimization in early adolescence. Journal of Early Adolescence, 30(4), 543-566. https://doi.org/10.1177/0272431609340517

Dijkstra, J. K., Lindenberg, S., & Veenstra, R. (2008). Beyond the class norm: Bullying behavior of popular adolescents and its relation to peer acceptance and rejection. Journal of Abnormal Child Psychology, 36(8), 1289-1299. https://doi.org/10.1007/s10802-008-9251-7

Faris, R., & Ennett, S. (2012). Adolescent aggression: The role of peer group status motives, peer aggression, and group characteristics. Social Networks, 34(4), 371-378. https://doi.org/10.1016/j.socnet.2010.06.003

Farmer, T. W., Estell, D. B., Bishop, J. L., O’Neal, K. K., & Cairns, B. D. (2003). Rejected bullies or popular leaders? The social relations of aggressive subtypes of rural African American early adolescents. Developmental Psychology, 39(6), 992-1004. https://doi.org/10.1037/0012-1649.39.6.992

Farrington, D. P., & Ttofi, M. (2009). School-based programs to reduce bullying and victimization: A systematic review. Campbell Systematic Reviews, 5(6), 1-148. https://doi.org/10.4073/csr.2009.6

Gini, G., & Pozzoli, T. (2009). Association between bullying and psychosomatic problems: A meta-analysis. Pediatrics, 123(3), 1059-1065. https://doi.org/10.1542/peds.2008-1215

Gladden, R. M., Vivolo-Kantor, A. M., Hamburger, M. E., & Lumpkin, C. D. (2014). Bullying surveillance among youths: Uniform definitions for public health and recommended data elements (Version 1.0). Centers for Disease Control and Prevention and U.S. Department of Education. https://files.eric.ed.gov/fulltext/ED575477.pdf

Glew, G. M., Fan, M.-Y., Katon, W., Rivara, F. P., & Kernic, M. A. (2005). Bullying, psychosocial adjustment, and academic performance in elementary school. Archives of Pediatrics and Adolescent Medicine, 159(11), 1026-1031. https://doi.org/10.1001/archpedi.159.11.1026

Hatzenbuehler, M. L., & Keyes, K. M. (2013). Inclusive anti-bullying policies and reduced risk of suicide attempts in lesbian and gay youth. Journal of Adolescent Health, 53(1), S21-S26. https://doi.org/10.1016/j.jadohealth.2012.08.010

Institute of Medicine. (1994). Reducing risks for mental disorders: Frontiers for preventive intervention research. National Academy Press.

Jiménez-Barbero, J. A., Ruiz-Hernández, J. A., Llor-Zaragoza, L., Pérez-García, M., & Llor- Esteban, B. (2016). Effectiveness of anti-bullying school programs: A meta-analysis. Children and Youth Services Review, 61, 165-175. https://doi.org/https://doi.org/10.1016/j.childyouth.2015.12.015

Juvonen, J., Nishina, A., & Graham, S. (2000). Peer harassment, psychological adjustment, and school functioning in early adolescence. Journal of Educational Psychology, 92(2), 349-359. https://doi.org/10.1037/0022-0663.92.2.349

Klomek, A. B., Kleinman, M., Altschuler, E., Marrocco, F., Amakawa, L., & Gould, M. S. (2011). High school bullying as a risk for later depression and suicidality. Suicide and Life-Threatening Behavior, 41(5), 501-516. https://doi.org/10.1111/j.1943-278X.2011.00046.x

Kochenderfer, B. J., & Ladd, G. W. (1996). Peer victimization: Cause or consequence of school maladjustment? Child Development, 67(4), 1305-1317. https://doi.org/10.1111/j.1467-8624.1996.tb01797.x

Kowalski, R. M., and Limber, S. P. (2013). Psychological, physical, and academic correlates of cyberbullying and traditional bullying. Journal of Adolescent Health, 53(1), S13-S20. https://doi.org/10.1016/j.jadohealth.2012.09.018

Lereya, S. T., Copeland, W. E., Costello, E. J., & Wolke, D. (2015). Adult mental health consequences of peer bullying and maltreatment in childhood: Two cohorts in two countries. The Lancet Psychiatry, 2(6), 524-531. https://doi.org/10.1016/S2215-0366(15)00165-0

Limber, S. B. (2014). Bullying Among Children and Youth. Unpublished manuscript of a study commissioned by the Planning Committee on Increasing Capacity for Reducing Bullying and Its Impact on the Lifecourse of Youth Involved. Department of Psychology and Institute on Family and Neighborhood Life, Clemson University.

Marsh, H. W., Nagengast, B., Morin, A. J., Parada, R. H., Craven, R. G., & Hamilton, L. R. (2011). Construct validity of the multidimensional structure of bullying and victimization: An application of exploratory structural equation modeling. Journal of Educational Psychology, 103(3), 701-732. https://doi.org/10.1037/a0024122

McDougall, P., & Vaillancourt, T. (2015). Long-term adult outcomes of peer victimization in childhood and adolescence: Pathways to adjustment and maladjustment. American Psychologist, 70(4), 300. https://doi.org/10.1037/a0039174

Minton, S. J. (2014). Prejudice and effective anti-bullying intervention: Evidence from the bullying of “minorities.” Nordic Psychology, 66(2), 108-120. https://doi.org/10.1080/19012276.2014.928485

Musher-Eizenman, D. R., Boxer, P., Danner, S., Dubow, E. F., Goldstein, S. E., & Heretick, D. M. L. (2004). Social-cognitive mediators of the relation of environmental and emotion regulation factors to children’s aggression. Aggressive Behavior, 30(5), 389-408.

Nakamoto, J., & Schwartz, D. (2010). Is peer victimization associated with academic achievement? A meta-analytic review. Social Development, 19(2), 221-242.

National Academies of Sciences, Engineering, and Medicine. 2016. Preventing bullying through science, policy, and practice. The National Academies Press. https://doi.org/10.17226/23482

Neary, A., & Joseph, S. (1994). Peer victimization and its relationship to self-concept and depression among schoolgirls. Personality and Individual Differences, 16(1), 183-186. https://doi.org/10.1016/0191-8869(94)90122-8

Nixon, C. (2014). Current perspectives: The impact of cyberbullying on adolescent health. Adolescent Health, Medicine and Therapeutics, 143-158. https://doi.org/10.2147/AHMT.S36456

Olweus, D. (1993a). Bullying at school. What we know and what we can do. Oxford, UK: Blackwell.

Olweus, D. (1993b). Victimization by peers: Antecedents and long-term outcomes. In K. H. Rubin & J. B. Asendorpf (Eds.), Social Withdrawal, Inhibition, and Shyness in Childhood (pp. 315-341). Psychology Press.

Olweus, D. (2001). Peer harassment: A critical analysis and some important issues. In J. Juvonen & S. Graham (Eds.), Peer harassment in school: The plight of the vulnerable and victimized (pp. 3-20). Guilford Press.

Peeters, M., Cillessen, A. H., & Scholte, R. H. (2010). Clueless or powerful? Identifying subtypes of bullies in adolescence. Journal of Youth and Adolescence, 39(9), 1041-1052. https://doi.org/10.1007/s10964-009-9478-9

Polanin, J. R., Espelage, D. L., & Pigott, T. D. (2012). A meta-analysis of school-based bullying prevention programs’ effects on bystander intervention behavior. School Psychology Review, 41(1), 47-65.

Pöyhönen, V., Juvonen, J., & Salmivalli, C. (2010). What does it take to stand up for the victim of bullying? The interplay between personal and social factors. Merrill-Palmer Quarterly, 56(2), 143-163.

Reijntjes, A., Kamphuis, J. H., Prinzie, P., & Telch, M. J. (2010). Peer victimization and internalizing problems in children: A meta-analysis of longitudinal studies. Child Abuse & Neglect, 34(4), 244-252. https://doi.org/10.1016/j.chiabu.2009.07.009

Rigby, K., & Slee, P. T. (1993). Dimensions of interpersonal relation among Australian children and implications for psychological well-being. Journal of Social Psychology, 133(1), 33-42. https://doi.org/10.1080/00224545.1993.9712116

Rigby, K., & Slee, P. (2008). Interventions to reduce bullying. International Journal of Adolescent Medicine and Health, 20(2), 165-183. https://doi.org/10.1515/IJAMH.2008.20.2.165

Rivers, I., & Noret, N. (2013). Potential suicide ideation and its association with observing bullying at school. Journal of Adolescent Health, 53(1), S32-S36. https://doi.org/10.1016/j.jadohealth.2012.10.279

Rivers, I., Poteat, V. P., Noret, N., & Ashurst, N. (2009). Observing bullying at school: The mental health implications of witness status. School Psychology Quarterly, 24(4), 211-223. https://doi.org/10.1037/a0018164

Rodkin, P. C., Espelage, D. L., & Hanish, L. D. (2015). A relational framework for understanding bullying: Developmental antecedents and outcomes. American Psychologist, 70(4), 311-321. https://doi.org/10.1037/a0038658

Salmivalli, C. (2001). Group view on victimization: Empirical findings and their implications. In J. Juvonen & S. Graham (Eds.), Peer harassment in school (pp. 398-419). Guilford Press.

Salmivalli, C. (2010). Bullying and the peer group: A review. Aggression and Violent Behavior, 15(2), 112-120. https://doi.org/10.1016/j.avb.2009.08.007

Salmivalli, C. (2014). Participant roles in bullying: How can peer bystanders be utilized in interventions? Theory Into Practice, 53(4), 286-292. https://doi.org/10.1080/00405841.2014.947222

Sijtsema, J. J., Veenstra, R., Lindenberg, S., & Salmivalli, C. (2009). Empirical test of bullies’ status goals: Assessing direct goals, aggression, and prestige. Aggressive Behavior, 35, 57-67. https://doi.org/10.1002/ab.20282

Thunfors, P., & Cornell, D. (2008). The popularity of middle school bullies. Journal of School Violence, 7(1), 65-82. https://doi.org/10.1300/J202v07n01_05

Trach, J., Hymel, S., Waterhouse, T., & Neale, K. (2010). Bystander responses to school bullying: A cross-sectional investigation of grade and sex differences. Canadian Journal of School Psychology, 25(1), 114-130. https://doi.org/10.1177/0829573509357553

Ttofi, M. M., & Farrington, D. P. (2009). What works in preventing bullying: Effective elements of anti-bullying programmes. Journal of Aggression, Conflict and Peace Research, 1(1), 13-24. https://doi.org/10.1108/17596599200900003

Ttofi, M. M., & Farrington, D. P. (2011). Effectiveness of school-based programs to reduce bullying: A systematic and meta-analytic review. Journal of Experimental Criminology, 7(1), 27-56. https://doi.org/10.1007/s11292-010-9109-1

Ttofi, M. M., Farrington, D. P., Lösel, F., & Loeber, R. (2011). Do the victims of school bullies tend to become depressed later in life? A systematic review and meta-analysis of longitudinal studies. Journal of Aggression, Conflict and Peace Research, 3(2), 63-73. https://doi.org/10.1108/17596591111132873/full/html

Vaillancourt, T., Hymel, S., & McDougall, P. (2003). Bullying is power: Implications for school-based intervention strategies. Journal of Applied School Psychology, 19(2), 157-176. https://doi.org/10.1300/J008v19n02_10

Vaillancourt, T., McDougall, P., Hymel, S., & Sunderani, S. (2010). The relationship between power and bullying behavior. In S. R. Jimerson, S. M. Swearer, & D. L. Espelage (Eds.), Handbook of Bullying in Schools: An International Perspective (pp. 211-222). Routledge.

Vaillancourt, T., Brittain, H. L., McDougall, P., & Duku, E. (2013). Longitudinal links between childhood peer victimization, internalizing and externalizing problems, and academic functioning: Developmental cascades. Journal of Abnormal Child Psychology, 41(8), 1203-1215. https://doi.org/10.1007/s10802-013-9781-5

Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121-127. https://doi.org/10.1016/j.jadohealth.2010.08.020

Vreeman, R. C., & Carroll, A. E. (2007). A systematic review of school-based interventions to prevent bullying. Archives of Pediatrics and Adolescent Medicine, 161(1), 78-88.

Wolke, D., & Lereya, S. T. (2015). Long-term effects of bullying. Archives of Disease in Childhood, 100(9), 879-85. https://doi.org/10.1136/archdischild-2014-306667

Wolke, D., Lereya, S. T., Fisher, H., Lewis, G., & Zammit, S. (2014). Bullying in elementary school and psychotic experiences at 18 years: A longitudinal, population-based cohort study. Psychological Medicine, 44(10, 2199-2211. https://doi.org/10.1017/S0033291713002912

Ybarra, M. L., & Mitchell, K. L. (2004). Online aggressor/targets, aggressors, and targets: A comparison of associated youth characteristics. Journal of Child Psychology and Psychiatry, 45(7), 1308-1316. https://doi.org/10.1111/j.1469-7610.2004.00328.x

Authors who publish with this journal agree to the following terms:

• The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
• Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
• Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
• The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post online a prepublication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
• Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
• the Work is the Author’s original work;
• the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
• the Work is not pending review or under consideration by another publisher;
• the Work has not previously been published;
• the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
• the Work contains no libel, invasion of privacy, or other unlawful matter.
• The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.

Revised 7/16/2018. Revision Description: Removed outdated link. 

Developed By

Information.

• For Readers
• For Authors
• For Librarians

Preventing Bullying Through Science, Policy, and Practice (2016)

Chapter: 1 introduction, 1 introduction.

Bullying, long tolerated by many as a rite of passage into adulthood, is now recognized as a major and preventable public health problem, one that can have long-lasting consequences ( McDougall and Vaillancourt, 2015 ; Wolke and Lereya, 2015 ). Those consequences—for those who are bullied, for the perpetrators of bullying, and for witnesses who are present during a bullying event—include poor school performance, anxiety, depression, and future delinquent and aggressive behavior. Federal, state, and local governments have responded by adopting laws and implementing programs to prevent bullying and deal with its consequences. However, many of these responses have been undertaken with little attention to what is known about bullying and its effects. Even the definition of bullying varies among both researchers and lawmakers, though it generally includes physical and verbal behavior, behavior leading to social isolation, and behavior that uses digital communications technology (cyberbullying). This report adopts the term “bullying behavior,” which is frequently used in the research field, to cover all of these behaviors.

Bullying behavior is evident as early as preschool, although it peaks during the middle school years ( Currie et al., 2012 ; Vaillancourt et al., 2010 ). It can occur in diverse social settings, including classrooms, school gyms and cafeterias, on school buses, and online. Bullying behavior affects not only the children and youth who are bullied, who bully, and who are both bullied and bully others but also bystanders to bullying incidents. Given the myriad situations in which bullying can occur and the many people who may be involved, identifying effective prevention programs and policies is challenging, and it is unlikely that any one approach will be ap-

propriate in all situations. Commonly used bullying prevention approaches include policies regarding acceptable behavior in schools and behavioral interventions to promote positive cultural norms.

STUDY CHARGE

Recognizing that bullying behavior is a major public health problem that demands the concerted and coordinated time and attention of parents, educators and school administrators, health care providers, policy makers, families, and others concerned with the care of children, a group of federal agencies and private foundations asked the National Academies of Sciences, Engineering, and Medicine to undertake a study of what is known and what needs to be known to further the field of preventing bullying behavior. The Committee on the Biological and Psychosocial Effects of Peer Victimization:

Lessons for Bullying Prevention was created to carry out this task under the Academies’ Board on Children, Youth, and Families and the Committee on Law and Justice. The study received financial support from the Centers for Disease Control and Prevention (CDC), the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the Health Resources and Services Administration, the Highmark Foundation, the National Institute of Justice, the Robert Wood Johnson Foundation, Semi J. and Ruth W. Begun Foundation, and the Substance Abuse and Mental Health Services Administration. The full statement of task for the committee is presented in Box 1-1 .

Although the committee acknowledges the importance of this topic as it pertains to all children in the United States and in U.S. territories, this report focuses on the 50 states and the District of Columbia. Also, while the committee acknowledges that bullying behavior occurs in the school

environment for youth in foster care, in juvenile justice facilities, and in other residential treatment facilities, this report does not address bullying behavior in those environments because it is beyond the study charge.

CONTEXT FOR THE STUDY

This section of the report highlights relevant work in the field and, later in the chapter under “The Committee’s Approach,” presents the conceptual framework and corresponding definitions of terms that the committee has adopted.

Historical Context

Bullying behavior was first characterized in the scientific literature as part of the childhood experience more than 100 years ago in “Teasing and Bullying,” published in the Pedagogical Seminary ( Burk, 1897 ). The author described bullying behavior, attempted to delineate causes and cures for the tormenting of others, and called for additional research ( Koo, 2007 ). Nearly a century later, Dan Olweus, a Swedish research professor of psychology in Norway, conducted an intensive study on bullying ( Olweus, 1978 ). The efforts of Olweus brought awareness to the issue and motivated other professionals to conduct their own research, thereby expanding and contributing to knowledge of bullying behavior. Since Olweus’s early work, research on bullying has steadily increased (see Farrington and Ttofi, 2009 ; Hymel and Swearer, 2015 ).

Over the past few decades, venues where bullying behavior occurs have expanded with the advent of the Internet, chat rooms, instant messaging, social media, and other forms of digital electronic communication. These modes of communication have provided a new communal avenue for bullying. While the media reports linking bullying to suicide suggest a causal relationship, the available research suggests that there are often multiple factors that contribute to a youth’s suicide-related ideology and behavior. Several studies, however, have demonstrated an association between bullying involvement and suicide-related ideology and behavior (see, e.g., Holt et al., 2015 ; Kim and Leventhal, 2008 ; Sourander, 2010 ; van Geel et al., 2014 ).

In 2013, the Health Resources and Services Administration of the U.S. Department of Health and Human Services requested that the Institute of Medicine 1 and the National Research Council convene an ad hoc planning committee to plan and conduct a 2-day public workshop to highlight relevant information and knowledge that could inform a multidisciplinary

___________________

1 Prior to 2015, the National Academy of Medicine was known as the Institute of Medicine.

road map on next steps for the field of bullying prevention. Content areas that were explored during the April 2014 workshop included the identification of conceptual models and interventions that have proven effective in decreasing bullying and the antecedents to bullying while increasing protective factors that mitigate the negative health impact of bullying. The discussions highlighted the need for a better understanding of the effectiveness of program interventions in realistic settings; the importance of understanding what works for whom and under what circumstances, as well as the influence of different mediators (i.e., what accounts for associations between variables) and moderators (i.e., what affects the direction or strength of associations between variables) in bullying prevention efforts; and the need for coordination among agencies to prevent and respond to bullying. The workshop summary ( Institute of Medicine and National Research Council, 2014c ) informs this committee’s work.

Federal Efforts to Address Bullying and Related Topics

Currently, there is no comprehensive federal statute that explicitly prohibits bullying among children and adolescents, including cyberbullying. However, in the wake of the growing concerns surrounding the implications of bullying, several federal initiatives do address bullying among children and adolescents, and although some of them do not primarily focus on bullying, they permit some funds to be used for bullying prevention purposes.

The earliest federal initiative was in 1999, when three agencies collaborated to establish the Safe Schools/Healthy Students initiative in response to a series of deadly school shootings in the late 1990s. The program is administered by the U.S. Departments of Education, Health and Human Services, and Justice to prevent youth violence and promote the healthy development of youth. It is jointly funded by the Department of Education and by the Department of Health and Human Services’ Substance Abuse and Mental Health Services Administration. The program has provided grantees with both the opportunity to benefit from collaboration and the tools to sustain it through deliberate planning, more cost-effective service delivery, and a broader funding base ( Substance Abuse and Mental Health Services Administration, 2015 ).

The next major effort was in 2010, when the Department of Education awarded $38.8 million in grants under the Safe and Supportive Schools (S3) Program to 11 states to support statewide measurement of conditions for learning and targeted programmatic interventions to improve conditions for learning, in order to help schools improve safety and reduce substance use. The S3 Program was administered by the Safe and Supportive Schools Group, which also administered the Safe and Drug-Free Schools and Communities Act State and Local Grants Program, authorized by the

1994 Elementary and Secondary Education Act. 2 It was one of several programs related to developing and maintaining safe, disciplined, and drug-free schools. In addition to the S3 grants program, the group administered a number of interagency agreements with a focus on (but not limited to) bullying, school recovery research, data collection, and drug and violence prevention activities ( U.S. Department of Education, 2015 ).

A collaborative effort among the U.S. Departments of Agriculture, Defense, Education, Health and Human Services, Interior, and Justice; the Federal Trade Commission; and the White House Initiative on Asian Americans and Pacific Islanders created the Federal Partners in Bullying Prevention (FPBP) Steering Committee. Led by the U.S. Department of Education, the FPBP works to coordinate policy, research, and communications on bullying topics. The FPBP Website provides extensive resources on bullying behavior, including information on what bullying is, its risk factors, its warning signs, and its effects. 3 The FPBP Steering Committee also plans to provide details on how to get help for those who have been bullied. It also was involved in creating the “Be More than a Bystander” Public Service Announcement campaign with the Ad Council to engage students in bullying prevention. To improve school climate and reduce rates of bullying nationwide, FPBP has sponsored four bullying prevention summits attended by education practitioners, policy makers, researchers, and federal officials.

In 2014, the National Institute of Justice—the scientific research arm of the U.S. Department of Justice—launched the Comprehensive School Safety Initiative with a congressional appropriation of $75 million. The funds are to be used for rigorous research to produce practical knowledge that can improve the safety of schools and students, including bullying prevention. The initiative is carried out through partnerships among researchers, educators, and other stakeholders, including law enforcement, behavioral and mental health professionals, courts, and other justice system professionals ( National Institute of Justice, 2015 ).

In 2015, the Every Student Succeeds Act was signed by President Obama, reauthorizing the 50-year-old Elementary and Secondary Education Act, which is committed to providing equal opportunities for all students. Although bullying is neither defined nor prohibited in this act, it is explicitly mentioned in regard to applicability of safe school funding, which it had not been in previous iterations of the Elementary and Secondary Education Act.

The above are examples of federal initiatives aimed at promoting the

2 The Safe and Drug-Free Schools and Communities Act was included as Title IV, Part A, of the 1994 Elementary and Secondary Education Act. See http://www.ojjdp.gov/pubs/gun_violence/sect08-i.html [October 2015].

3 For details, see http://www.stopbullying.gov/ [October 2015].

healthy development of youth, improving the safety of schools and students, and reducing rates of bullying behavior. There are several other federal initiatives that address student bullying directly or allow funds to be used for bullying prevention activities.

Definitional Context

The terms “bullying,” “harassment,” and “peer victimization” have been used in the scientific literature to refer to behavior that is aggressive, is carried out repeatedly and over time, and occurs in an interpersonal relationship where a power imbalance exists ( Eisenberg and Aalsma, 2005 ). Although some of these terms have been used interchangeably in the literature, peer victimization is targeted aggressive behavior of one child against another that causes physical, emotional, social, or psychological harm. While conflict and bullying among siblings are important in their own right ( Tanrikulu and Campbell, 2015 ), this area falls outside of the scope of the committee’s charge. Sibling conflict and aggression falls under the broader concept of interpersonal aggression, which includes dating violence, sexual assault, and sibling violence, in addition to bullying as defined for this report. Olweus (1993) noted that bullying, unlike other forms of peer victimization where the children involved are equally matched, involves a power imbalance between the perpetrator and the target, where the target has difficulty defending him or herself and feels helpless against the aggressor. This power imbalance is typically considered a defining feature of bullying, which distinguishes this particular form of aggression from other forms, and is typically repeated in multiple bullying incidents involving the same individuals over time ( Olweus, 1993 ).

Bullying and violence are subcategories of aggressive behavior that overlap ( Olweus, 1996 ). There are situations in which violence is used in the context of bullying. However, not all forms of bullying (e.g., rumor spreading) involve violent behavior. The committee also acknowledges that perspective about intentions can matter and that in many situations, there may be at least two plausible perceptions involved in the bullying behavior.

A number of factors may influence one’s perception of the term “bullying” ( Smith and Monks, 2008 ). Children and adolescents’ understanding of the term “bullying” may be subject to cultural interpretations or translations of the term ( Hopkins et al., 2013 ). Studies have also shown that influences on children’s understanding of bullying include the child’s experiences as he or she matures and whether the child witnesses the bullying behavior of others ( Hellström et al., 2015 ; Monks and Smith, 2006 ; Smith and Monks, 2008 ).

In 2010, the FPBP Steering Committee convened its first summit, which brought together more than 150 nonprofit and corporate leaders,

researchers, practitioners, parents, and youths to identify challenges in bullying prevention. Discussions at the summit revealed inconsistencies in the definition of bullying behavior and the need to create a uniform definition of bullying. Subsequently, a review of the 2011 CDC publication of assessment tools used to measure bullying among youth ( Hamburger et al., 2011 ) revealed inconsistent definitions of bullying and diverse measurement strategies. Those inconsistencies and diverse measurements make it difficult to compare the prevalence of bullying across studies ( Vivolo et al., 2011 ) and complicate the task of distinguishing bullying from other types of aggression between youths. A uniform definition can support the consistent tracking of bullying behavior over time, facilitate the comparison of bullying prevalence rates and associated risk and protective factors across different data collection systems, and enable the collection of comparable information on the performance of bullying intervention and prevention programs across contexts ( Gladden et al., 2014 ). The CDC and U.S. Department of Education collaborated on the creation of the following uniform definition of bullying (quoted in Gladden et al., 2014, p. 7 ):

Bullying is any unwanted aggressive behavior(s) by another youth or group of youths who are not siblings or current dating partners that involves an observed or perceived power imbalance and is repeated multiple times or is highly likely to be repeated. Bullying may inflict harm or distress on the targeted youth including physical, psychological, social, or educational harm.

This report noted that the definition includes school-age individuals ages 5-18 and explicitly excludes sibling violence and violence that occurs in the context of a dating or intimate relationship ( Gladden et al., 2014 ). This definition also highlighted that there are direct and indirect modes of bullying, as well as different types of bullying. Direct bullying involves “aggressive behavior(s) that occur in the presence of the targeted youth”; indirect bullying includes “aggressive behavior(s) that are not directly communicated to the targeted youth” ( Gladden et al., 2014, p. 7 ). The direct forms of violence (e.g., sibling violence, teen dating violence, intimate partner violence) can include aggression that is physical, sexual, or psychological, but the context and uniquely dynamic nature of the relationship between the target and the perpetrator in which these acts occur is different from that of peer bullying. Examples of direct bullying include pushing, hitting, verbal taunting, or direct written communication. A common form of indirect bullying is spreading rumors. Four different types of bullying are commonly identified—physical, verbal, relational, and damage to property. Some observational studies have shown that the different forms of bullying that youths commonly experience may overlap ( Bradshaw et al., 2015 ;

Godleski et al., 2015 ). The four types of bullying are defined as follows ( Gladden et al., 2014 ):

• Physical bullying involves the use of physical force (e.g., shoving, hitting, spitting, pushing, and tripping).
• Verbal bullying involves oral or written communication that causes harm (e.g., taunting, name calling, offensive notes or hand gestures, verbal threats).
• Relational bullying is behavior “designed to harm the reputation and relationships of the targeted youth (e.g., social isolation, rumor spreading, posting derogatory comments or pictures online).”
• Damage to property is “theft, alteration, or damaging of the target youth’s property by the perpetrator to cause harm.”

In recent years, a new form of aggression or bullying has emerged, labeled “cyberbullying,” in which the aggression occurs through modern technological devices, specifically mobile phones or the Internet ( Slonje and Smith, 2008 ). Cyberbullying may take the form of mean or nasty messages or comments, rumor spreading through posts or creation of groups, and exclusion by groups of peers online.

While the CDC definition identifies bullying that occurs using technology as electronic bullying and views that as a context or location where bullying occurs, one of the major challenges in the field is how to conceptualize and define cyberbullying ( Tokunaga, 2010 ). The extent to which the CDC definition can be applied to cyberbullying is unclear, particularly with respect to several key concepts within the CDC definition. First, whether determination of an interaction as “wanted” or “unwanted” or whether communication was intended to be harmful can be challenging to assess in the absence of important in-person socioemotional cues (e.g., vocal tone, facial expressions). Second, assessing “repetition” is challenging in that a single harmful act on the Internet has the potential to be shared or viewed multiple times ( Sticca and Perren, 2013 ). Third, cyberbullying can involve a less powerful peer using technological tools to bully a peer who is perceived to have more power. In this manner, technology may provide the tools that create a power imbalance, in contrast to traditional bullying, which typically involves an existing power imbalance.

A study that used focus groups with college students to discuss whether the CDC definition applied to cyberbullying found that students were wary of applying the definition due to their perception that cyberbullying often involves less emphasis on aggression, intention, and repetition than other forms of bullying ( Kota et al., 2014 ). Many researchers have responded to this lack of conceptual and definitional clarity by creating their own measures to assess cyberbullying. It is noteworthy that very few of these

definitions and measures include the components of traditional bullying—i.e., repetition, power imbalance, and intent ( Berne et al., 2013 ). A more recent study argues that the term “cyberbullying” should be reserved for incidents that involve key aspects of bullying such as repetition and differential power ( Ybarra et al., 2014 ).

Although the formulation of a uniform definition of bullying appears to be a step in the right direction for the field of bullying prevention, there are some limitations of the CDC definition. For example, some researchers find the focus on school-age youth as well as the repeated nature of bullying to be rather limiting; similarly the exclusion of bullying in the context of sibling relationships or dating relationships may preclude full appreciation of the range of aggressive behaviors that may co-occur with or constitute bullying behavior. As noted above, other researchers have raised concerns about whether cyberbullying should be considered a particular form or mode under the broader heading of bullying as suggested in the CDC definition, or whether a separate defintion is needed. Furthermore, the measurement of bullying prevalence using such a definiton of bullying is rather complex and does not lend itself well to large-scale survey research. The CDC definition was intended to inform public health surveillance efforts, rather than to serve as a definition for policy. However, increased alignment between bullying definitions used by policy makers and researchers would greatly advance the field. Much of the extant research on bullying has not applied a consistent definition or one that aligns with the CDC definition. As a result of these and other challenges to the CDC definition, thus far there has been inconsistent adoption of this particular definition by researchers, practitioners, or policy makers; however, as the definition was created in 2014, less than 2 years is not a sufficient amount of time to assess whether it has been successfully adopted or will be in the future.

THE COMMITTEE’S APPROACH

This report builds on the April 2014 workshop, summarized in Building Capacity to Reduce Bullying: Workshop Summary ( Institute of Medicine and National Research Council, 2014c ). The committee’s work was accomplished over an 18-month period that began in October 2014, after the workshop was held and the formal summary of it had been released. The study committee members represented expertise in communication technology, criminology, developmental and clinical psychology, education, mental health, neurobiological development, pediatrics, public health, school administration, school district policy, and state law and policy. (See Appendix E for biographical sketches of the committee members and staff.) The committee met three times in person and conducted other meetings by teleconferences and electronic communication.

Information Gathering

The committee conducted an extensive review of the literature pertaining to peer victimization and bullying. In some instances, the committee drew upon the broader literature on aggression and violence. The review began with an English-language literature search of online databases, including ERIC, Google Scholar, Lexis Law Reviews Database, Medline, PubMed, Scopus, PsycInfo, and Web of Science, and was expanded as literature and resources from other countries were identified by committee members and project staff as relevant. The committee drew upon the early childhood literature since there is substantial evidence indicating that bullying involvement happens as early as preschool (see Vlachou et al., 2011 ). The committee also drew on the literature on late adolescence and looked at related areas of research such as maltreatment for insights into this emerging field.

The committee used a variety of sources to supplement its review of the literature. The committee held two public information-gathering sessions, one with the study sponsors and the second with experts on the neurobiology of bullying; bullying as a group phenomenon and the role of bystanders; the role of media in bullying prevention; and the intersection of social science, the law, and bullying and peer victimization. See Appendix A for the agendas for these two sessions. To explore different facets of bullying and give perspectives from the field, a subgroup of the committee and study staff also conducted a site visit to a northeastern city, where they convened four stakeholder groups comprised, respectively, of local practitioners, school personnel, private foundation representatives, and young adults. The site visit provided the committee with an opportunity for place-based learning about bullying prevention programs and best practices. Each focus group was transcribed and summarized thematically in accordance with this report’s chapter considerations. Themes related to the chapters are displayed throughout the report in boxes titled “Perspectives from the Field”; these boxes reflect responses synthesized from all four focus groups. See Appendix B for the site visit’s agenda and for summaries of the focus groups.

The committee also benefited from earlier reports by the National Academies of Sciences, Engineering, and Medicine through its Division of Behavioral and Social Sciences and Education and the Institute of Medicine, most notably:

• Reducing Risks for Mental Disorders: Frontiers for Preventive Intervention Research ( Institute of Medicine, 1994 )
• Community Programs to Promote Youth Development ( National Research Council and Institute of Medicine, 2002 )
• Deadly Lessons: Understanding Lethal School Violence ( National Research Council and Institute of Medicine, 2003 )
• Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities ( National Research Council and Institute of Medicine, 2009 )
• The Science of Adolescent Risk-Taking: Workshop Report ( Institute of Medicine and National Research Council, 2011 )
• Communications and Technology for Violence Prevention: Workshop Summary ( Institute of Medicine and National Research Council, 2012 )
• Building Capacity to Reduce Bullying: Workshop Summary ( Institute of Medicine and National Research Council, 2014c )
• The Evidence for Violence Prevention across the Lifespan and Around the World: Workshop Summary ( Institute of Medicine and National Research Council, 2014a )
• Strategies for Scaling Effective Family-Focused Preventive Interventions to Promote Children’s Cognitive, Affective, and Behavioral Health: Workshop Summary ( Institute of Medicine and National Research Council, 2014b )
• Investing in the Health and Well-Being of Young Adults ( Institute of Medicine and National Research Council, 2015 )

Although these past reports and workshop summaries address various forms of violence and victimization, this report is the first consensus study by the National Academies of Sciences, Engineering, and Medicine on the state of the science on the biological and psychosocial consequences of bullying and the risk and protective factors that either increase or decrease bullying behavior and its consequences.

Terminology

Given the variable use of the terms “bullying” and “peer victimization” in both the research-based and practice-based literature, the committee chose to use the current CDC definition quoted above ( Gladden et al., 2014, p. 7 ). While the committee determined that this was the best definition to use, it acknowledges that this definition is not necessarily the most user-friendly definition for students and has the potential to cause problems for students reporting bullying. Not only does this definition provide detail on the common elements of bullying behavior but it also was developed with input from a panel of researchers and practitioners. The committee also followed the CDC in focusing primarily on individuals between the ages of 5 and 18. The committee recognizes that children’s development occurs on a continuum, and so while it relied primarily on the CDC defini-

tion, its work and this report acknowledge the importance of addressing bullying in both early childhood and emerging adulthood. For purposes of this report, the committee used the terms “early childhood” to refer to ages 1-4, “middle childhood” for ages 5 to 10, “early adolescence” for ages 11-14, “middle adolescence” for ages 15-17, and “late adolescence” for ages 18-21. This terminology and the associated age ranges are consistent with the Bright Futures and American Academy of Pediatrics definition of the stages of development. 4

A given instance of bullying behavior involves at least two unequal roles: one or more individuals who perpetrate the behavior (the perpetrator in this instance) and at least one individual who is bullied (the target in this instance). To avoid labeling and potentially further stigmatizing individuals with the terms “bully” and “victim,” which are sometimes viewed as traits of persons rather than role descriptions in a particular instance of behavior, the committee decided to use “individual who is bullied” to refer to the target of a bullying instance or pattern and “individual who bullies” to refer to the perpetrator of a bullying instance or pattern. Thus, “individual who is bullied and bullies others” can refer to one who is either perpetrating a bullying behavior or a target of bullying behavior, depending on the incident. This terminology is consistent with the approach used by the FPBP (see above). Also, bullying is a dynamic social interaction ( Espelage and Swearer, 2003 ) where individuals can play different roles in bullying interactions based on both individual and contextual factors.

The committee used “cyberbullying” to refer to bullying that takes place using technology or digital electronic means. “Digital electronic forms of contact” comprise a broad category that may include e-mail, blogs, social networking Websites, online games, chat rooms, forums, instant messaging, Skype, text messaging, and mobile phone pictures. The committee uses the term “traditional bullying” to refer to bullying behavior that is not cyberbullying (to aid in comparisons), recognizing that the term has been used at times in slightly different senses in the literature.

Where accurate reporting of study findings requires use of the above terms but with senses different from those specified here, the committee has noted the sense in which the source used the term. Similarly, accurate reporting has at times required use of terms such as “victimization” or “victim” that the committee has chosen to avoid in its own statements.

4 For details on these stages of adolescence, see https://brightfutures.aap.org/Bright%20Futures%20Documents/3-Promoting_Child_Development.pdf [October 2015].

ORGANIZATION OF THE REPORT

This report is organized into seven chapters. After this introductory chapter, Chapter 2 provides a broad overview of the scope of the problem.

Chapter 3 focuses on the conceptual frameworks for the study and the developmental trajectory of the child who is bullied, the child who bullies, and the child who is bullied and also bullies. It explores processes that can explain heterogeneity in bullying outcomes by focusing on contextual processes that moderate the effect of individual characteristics on bullying behavior.

Chapter 4 discusses the cyclical nature of bullying and the consequences of bullying behavior. It summarizes what is known about the psychosocial, physical health, neurobiological, academic-performance, and population-level consequences of bullying.

Chapter 5 provides an overview of the landscape in bullying prevention programming. This chapter describes in detail the context for preventive interventions and the specific actions that various stakeholders can take to achieve a coordinated response to bullying behavior. The chapter uses the Institute of Medicine’s multi-tiered framework ( National Research Council and Institute of Medicine, 2009 ) to present the different levels of approaches to preventing bullying behavior.

Chapter 6 reviews what is known about federal, state, and local laws and policies and their impact on bullying.

After a critical review of the relevant research and practice-based literatures, Chapter 7 discusses the committee conclusions and recommendations and provides a path forward for bullying prevention.

The report includes a number of appendixes. Appendix A includes meeting agendas of the committee’s public information-gathering meetings. Appendix B includes the agenda and summaries of the site visit. Appendix C includes summaries of bullying prevalence data from the national surveys discussed in Chapter 2 . Appendix D provides a list of selected federal resources on bullying for parents and teachers. Appendix E provides biographical sketches of the committee members and project staff.

Berne, S., Frisén, A., Schultze-Krumbholz, A., Scheithauer, H., Naruskov, K., Luik, P., Katzer, C., Erentaite, R., and Zukauskiene, R. (2013). Cyberbullying assessment instruments: A systematic review. Aggression and Violent Behavior, 18 (2), 320-334.

Bradshaw, C.P., Waasdorp, T.E., and Johnson, S.L. (2015). Overlapping verbal, relational, physical, and electronic forms of bullying in adolescence: Influence of school context. Journal of Clinical Child & Adolescent Psychology, 44 (3), 494-508.

Burk, F.L. (1897). Teasing and bullying. The Pedagogical Seminary, 4 (3), 336-371.

Currie, C., Zanotti, C., Morgan, A., Currie, D., de Looze, M., Roberts, C., Samdal, O., Smith, O.R., and Barnekow, V. (2012). Social determinants of health and well-being among young people. Copenhagen, Denmark: World Health Organization Regional Office for Europe.

Eisenberg, M.E., and Aalsma, M.C. (2005). Bullying and peer victimization: Position paper of the Society for Adolescent Medicine. Journal of Adolescent Health, 36 (1), 88-91.

Espelage, D.L., and Swearer, S.M. (2003). Research on school bullying and victimization: What have we learned and where do we go from here? School Psychology Review, 32 (3), 365-383.

Farrington, D., and Ttofi, M. (2009). School-based programs to reduce bullying and victimization: A systematic review. Campbell Systematic Reviews, 5 (6).

Finkelhor, D., Ormrod, R.K., and Turner, H.A. (2007). Poly-victimization: A neglected component in child victimization. Child Abuse & Neglect , 31 (1), 7-26.

Gladden, R.M., Vivolo-Kantor, A.M., Hamburger, M.E., and Lumpkin, C.D. (2014). Bullying Surveillance among Youths: Uniform Definitions for Public Health and Recommended Data Elements, Version 1.0 . Atlanta, GA: Centers for Disease Control and Prevention and U.S. Department of Education.

Godleski, S.A., Kamper, K.E., Ostrov, J.M., Hart, E.J., and Blakely-McClure, S.J. (2015). Peer victimization and peer rejection during early childhood. Journal of Clinical Child & Adolescent Psychology, 44 (3), 380-392.

Hamburger, M.E., Basile, K.C., and Vivolo, A.M. (2011). Measuring Bullying Victimization, Perpetration, and Bystander Experiences: A Compendium of Assessment Tools. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control.

Hellström, L., Persson, L., and Hagquist, C. (2015). Understanding and defining bullying—Adolescents’ own views. Archives of Public Health, 73 (4), 1-9.

Holt, M.K., Vivolo-Kantor, A.M., Polanin, J.R., Holland, K.M., DeGue, S., Matjasko, J.L., Wolfe, M., and Reid, G. (2015). Bullying and suicidal ideation and behaviors: A meta-analysis. Pediatrics, 135 (2), e496-e509.

Hopkins, L., Taylor, L., Bowen, E., and Wood, C. (2013). A qualitative study investigating adolescents’ understanding of aggression, bullying and violence. Children and Youth Services Review, 35 (4), 685-693.

Hymel, S., and Swearer, S.M. (2015). Four decades of research on school bullying: An introduction. American Psychologist, 70 (4), 293.

Institute of Medicine. (1994). Reducing Risks for Mental Disorders: Frontiers for Preventive Intervention Research. Committee on Prevention of Mental Disorders. P.J. Mrazek and R.J. Haggerty, Editors. Division of Biobehavioral Sciences and Mental Disorders. Washington, DC: National Academy Press.

Institute of Medicine and National Research Council. (2011). The Science of Adolescent Risk-taking: Workshop Report . Committee on the Science of Adolescence. Washington, DC: The National Academies Press.

Institute of Medicine and National Research Council. (2012). Communications and Technology for Violence Prevention: Workshop Summary . Washington, DC: The National Academies Press.

Institute of Medicine and National Research Council. (2014a). The Evidence for Violence Prevention across the Lifespan and around the World: Workshop Summary . Washington, DC: The National Academies Press.

Institute of Medicine and National Research Council. (2014b). Strategies for Scaling Effective Family-Focused Preventive Interventions to Promote Children’s Cognitive, Affective, and Behavioral Health: Workshop Summary . Washington, DC: The National Academies Press.

Institute of Medicine and National Research Council. (2014c). Building Capacity to Reduce Bullying: Workshop Summary . Washington, DC: The National Academies Press.

Institute of Medicine and National Research Council. (2015). Investing in the Health and Well-Being of Young Adults . Washington, DC: The National Academies Press.

Kim, Y.S., and Leventhal, B. (2008). Bullying and suicide. A review. International Journal of Adolescent Medicine and Health, 20 (2), 133-154.

Koo, H. (2007). A time line of the evolution of school bullying in differing social contexts. Asia Pacific Education Review, 8 (1), 107-116.

Kota, R., Schoohs, S., Benson, M., and Moreno, M.A. (2014). Characterizing cyberbullying among college students: Hacking, dirty laundry, and mocking. Societies, 4 (4), 549-560.

McDougall, P., and Vaillancourt, T. (2015). Long-term adult outcomes of peer victimization in childhood and adolescence: Pathways to adjustment and maladjustment. American Psychologist, 70 (4), 300.

Monks, C.P., and Smith, P.K. (2006). Definitions of bullying: Age differences in understanding of the term and the role of experience. British Journal of Developmental Psychology, 24 (4), 801-821.

National Institute of Justice. (2015). Comprehensive School Safety Initiative. 2015. Available: http://nij.gov/topics/crime/school-crime/Pages/school-safety-initiative.aspx#about [October 2015].

National Research Council and Institute of Medicine. (2002). Community Programs to Promote Youth Development . Committee on Community-Level Programs for Youth. J. Eccles and J.A. Gootman, Editors. Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

National Research Council and Institute of Medicine. (2003). Deadly Lessons: Understanding Lethal School Violence . Case Studies of School Violence Committee. M.H. Moore, C.V. Petrie, A.A. Barga, and B.L. McLaughlin, Editors. Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council and Institute of Medicine. (2009). Preventing Mental, Emotional, and Behavioral Disorders among Young People: Progress and Possibilities. Committee on the Prevention of Mental Disorders and Substance Abuse Among Children, Youth, and Young Adults: Research Advances and Promising Interventions. M.E. O’Connell, T. Boat, and K.E. Warner, Editors. Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Olweus, D. (1978). Aggression in the Schools: Bullies and Whipping Boys. Washington, DC: Hemisphere.

Olweus, D. (1993). Bullying at School. What We Know and Whal We Can Do. Oxford, UK: Blackwell.

Olweus, D. (1996). Bully/victim problems in school. Prospects, 26 (2), 331-359.

Slonje, R., and Smith, P.K. (2008). Cyberbullying: Another main type of bullying? Scandinavian Journal of Psychology, 49 (2), 147-154.

Smith, P. ., and Monks, C. . (2008). Concepts of bullying: Developmental and cultural aspects. International Journal of Adolescent Medicine and Health, 20 (2), 101-112.

Sourander, A. (2010). The association of suicide and bullying in childhood to young adulthood: A review of cross-sectional and longitudinal research findings. Canadian Journal of Psychiatry, 55 (5), 282.

Sticca, F., and Perren, S. (2013). Is cyberbullying worse than traditional bullying? Examining the differential roles of medium, publicity, and anonymity for the perceived severity of bullying. Journal of Youth and Adolescence, 42 (5), 739-750.

Substance Abuse and Mental Health Services Administration. (2015). Safe Schools/Healthy Students. 2015. Available: http://www.samhsa.gov/safe-schools-healthy-students/about [November 2015].

Tanrikulu, I., and Campbell, M. (2015). Correlates of traditional bullying and cyberbullying perpetration among Australian students. Children and Youth Services Review , 55 , 138-146.

Tokunaga, R.S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior, 26 (3), 277-287.

U.S. Department of Education. (2015). Safe and Supportive Schools . Available: http://www.ed.gov/news/press-releases/us-department-education-awards-388-million-safe-and-supportive-school-grants [October 2015].

Vaillancourt, T., Trinh, V., McDougall, P., Duku, E., Cunningham, L., Cunningham, C., Hymel, S., and Short, K. (2010). Optimizing population screening of bullying in school-aged children. Journal of School Violence, 9 (3), 233-250.

van Geel, M., Vedder, P., and Tanilon, J. (2014). Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: A meta-analysis. Journal of the American Medical Association. Pediatrics, 168 (5), 435-442.

Vivolo, A.M., Holt, M.K., and Massetti, G.M. (2011). Individual and contextual factors for bullying and peer victimization: Implications for prevention. Journal of School Violence, 10 (2), 201-212.

Vlachou, M., Andreou, E., Botsoglou, K., and Didaskalou, E. (2011). Bully/victim problems among preschool children: A review of current research evidence. Educational Psychology Review, 23 (3), 329-358.

Wolke, D., and Lereya, S.T. (2015). Long-term effects of bullying. Archives of Disease in Childhood, 100 (9), 879-885.

Ybarra, M.L., Espelage, D.L., and Mitchell, K.J. (2014). Differentiating youth who are bullied from other victims of peer-aggression: The importance of differential power and repetition. Journal of Adolescent Health, 55 (2), 293-300.

This page intentionally left blank.

Bullying has long been tolerated as a rite of passage among children and adolescents. There is an implication that individuals who are bullied must have "asked for" this type of treatment, or deserved it. Sometimes, even the child who is bullied begins to internalize this idea. For many years, there has been a general acceptance and collective shrug when it comes to a child or adolescent with greater social capital or power pushing around a child perceived as subordinate. But bullying is not developmentally appropriate; it should not be considered a normal part of the typical social grouping that occurs throughout a child's life.

Although bullying behavior endures through generations, the milieu is changing. Historically, bulling has occurred at school, the physical setting in which most of childhood is centered and the primary source for peer group formation. In recent years, however, the physical setting is not the only place bullying is occurring. Technology allows for an entirely new type of digital electronic aggression, cyberbullying, which takes place through chat rooms, instant messaging, social media, and other forms of digital electronic communication.

Composition of peer groups, shifting demographics, changing societal norms, and modern technology are contextual factors that must be considered to understand and effectively react to bullying in the United States. Youth are embedded in multiple contexts and each of these contexts interacts with individual characteristics of youth in ways that either exacerbate or attenuate the association between these individual characteristics and bullying perpetration or victimization. Recognizing that bullying behavior is a major public health problem that demands the concerted and coordinated time and attention of parents, educators and school administrators, health care providers, policy makers, families, and others concerned with the care of children, this report evaluates the state of the science on biological and psychosocial consequences of peer victimization and the risk and protective factors that either increase or decrease peer victimization behavior and consequences.

READ FREE ONLINE

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

Bullying: What We Know Based On 40 Years of Research

APA journal examines science aimed at understanding causes, prevention

WASHINGTON — A special issue of American Psychologist ® provides a comprehensive review of over 40 years of research on bullying among school age youth, documenting the current understanding of the complexity of the issue and suggesting directions for future research.

“The lore of bullies has long permeated literature and popular culture. Yet bullying as a distinct form of interpersonal aggression was not systematically studied until the 1970s. Attention to the topic has since grown exponentially,” said Shelley Hymel, PhD, professor of human development, learning and culture at the University of British Columbia, a scholarly lead on the special issue along with Susan M. Swearer, PhD, professor of school psychology at the University of Nebraska-Lincoln. “Inspired by the 2011 U.S. White House Conference on Bullying Prevention, this collection of articles documents current understanding of school bullying.”

The special issue consists of an introductory overview  (PDF, 90KB) by Hymel and Swearer, co-directors of the Bullying Research Network, and five articles on various research areas of bullying including the long-term effects of bullying into adulthood, reasons children bully others, the effects of anti-bullying laws and ways of translating research into anti-bullying practice.

Articles in the issue:

Long-Term Adult Outcomes of Peer Victimization in Childhood and Adolescence: Pathways to Adjustment and Maladjustment  (PDF, 122KB) by Patricia McDougall, PhD, University of Saskatchewan, and Tracy Vaillancourt, PhD, University of Ottawa.

The experience of being bullied is painful and difficult. Its negative impact — on academic functioning, physical and mental health, social relationships and self-perceptions — can endure across the school years. But not every victimized child develops into a maladjusted adult. In this article, the authors provide an overview of the negative outcomes experienced by victims through childhood and adolescence and sometimes into adulthood. They then analyze findings from prospective studies to identify factors that lead to different outcomes in different people, including in their biology, timing, support systems and self-perception.

Patricia McDougall can be contacted by email or by phone at (306) 966-6203.

A Relational Framework for Understanding Bullying: Developmental Antecedents and Outcomes  (PDF, 151KB) by Philip Rodkin, PhD, and Dorothy Espelage, PhD, University of Illinois, Urbana-Champaign, and Laura Hanish, PhD, Arizona State University.

How do you distinguish bullying from aggression in general? In this review, the authors describe bullying from a relationship perspective. In order for bullying to be distinguished from other forms of aggression, a relationship must exist between the bully and the victim, there must be an imbalance of power between the two and it must take place over a period of time. “Bullying is perpetrated within a relationship, albeit a coercive, unequal, asymmetric relationship characterized by aggression,” wrote the authors. Within that perspective, the image of bullies as socially incompetent youth who rely on physical coercion to resolve conflicts is nothing more than a stereotype. While this type of “bully-victim” does exist and is primarily male, the authors describe another type of bully who is more socially integrated and has surprisingly high levels of popularity among his or her peers. As for the gender of victims, bullying is just as likely to occur between boys and girls as it is to occur in same-gender groups.  

Dorothy Espelage can be contacted by email or by phone at (217) 333-9139.

Translating Research to Practice in Bullying Prevention  (PDF, 157KB) by Catherine Bradshaw, PhD, University of Virginia.

This paper reviews the research and related science to develop a set of recommendations for effective bullying prevention programs. From mixed findings on existing programs, the author identifies core elements of promising prevention approaches (e.g., close playground supervision, family involvement, and consistent classroom management strategies) and recommends a three-tiered public health approach that can attend to students at all risk levels. However, the author notes, prevention efforts must be sustained and integrated to effect change. 

Catherine Bradshaw can be contacted by email or by phone at (434) 924-8121.

Law and Policy on the Concept of Bullying at School  (PDF, 126KB) by Dewey Cornell, PhD, University of Virginia, and Susan Limber, PhD, Clemson University.

Since the shooting at Columbine High School in 1999, all states but one have passed anti-bullying laws, and multiple court decisions have made schools more accountable for peer victimization. Unfortunately, current legal and policy approaches, which are strongly rooted in laws regarding harassment and discrimination, do not provide adequate protection for all bullied students. In this article, the authors provide a review of the legal framework underpinning many anti-bullying laws and make recommendations on best practices for legislation and school policies to effectively address the problem of bullying.

Dewey Cornell can be contacted by email or by phone at (434) 924-0793.

Understanding the Psychology of Bullying: Moving Toward a Social-Ecological Diathesis-Stress Model by Susan Swearer, PhD, University of Nebraska-Lincoln, and Shelley Hymel, PhD, University of British Columbia.

Children’s involvement in bullying varies across roles and over time. A student may be victimized by classmates but bully a sibling at home. Bullying is a complex form of interpersonal aggression that can be both a one-on-one process and a group phenomenon. It negatively affects not only the victim, but the bully and witnesses as well. In this paper, the authors suggest an integrated model for examining bullying and victimization that recognizes the complex and dynamic nature of bullying across multiple settings over time.

Susan Swearer  can be contacted by email or by phone at (402) 472-1741. Shelley Hymel can be contacted by email or by phone at (604) 822-6022.

Copies of articles are also available from APA Public Affairs , (202) 336-5700.

The American Psychological Association, in Washington, D.C., is the largest scientific and professional organization representing psychology in the United States. APA's membership includes more than 122,500 researchers, educators, clinicians, consultants and students. Through its divisions in 54 subfields of psychology and affiliations with 60 state, territorial and Canadian provincial associations, APA works to advance the creation, communication and application of psychological knowledge to benefit society and improve people's lives.

Jim Sliwa (202) 336-5707

• Open access
• Published: 14 December 2023

Online and school bullying roles: are bully-victims more vulnerable in nonsuicidal self-injury and in psychological symptoms than bullies and victims?

• Boglárka Drubina   ORCID: orcid.org/0000-0002-3930-4293 1 , 2 ,
• Gyöngyi Kökönyei   ORCID: orcid.org/0000-0001-6750-2644 2 , 3 , 4 ,
• Dóra Várnai 2 , 5 &
• Melinda Reinhardt   ORCID: orcid.org/0000-0001-7010-5623 2 , 6  

BMC Psychiatry volume  23 , Article number:  945 ( 2023 ) Cite this article

1530 Accesses

2 Citations

3 Altmetric

Metrics details

Bullying leads to adverse mental health outcomes and it has also been linked to nonsuicidal self-injury (NSSI) in community adolescents. It is not clear whether different roles of bullying (bully, victim, bully-victim) are associated with NSSI, furthermore the same associations in cyberbullying are even less investigated.

The aim of the current study was to test whether students involved in school or online bullying differed from their not involved peers and from each other in psychological symptoms (externalizing and internalizing problems) and in NSSI severity (number of episodes, number of methods). Furthermore, mediation models were tested to explore the possible role of externalizing and internalizing problems in the association of school and online bullying roles with NSSI. In our study, 1011 high school students (66.07% girls; n = 668), aged between 14 and 20 years (M age = 16.81; SD = 1.41) participated.

Lifetime prevalence of at least one episode of NSSI was 41.05% (n = 415). Students involved in bullying used more methods of NSSI than not involved adolescents. In general, victim status was associated mostly with internalizing symptoms, while bully role was more strongly associated with externalizing problems. Bully-victims status was associated with both types of psychological problems, but this group did not show a significantly elevated NSSI severity compared to other bullying roles. Externalizing and internalizing problems mediated the relationship between bullying roles and NSSI with different paths at different roles, especially in case of current NSSI that happened in the previous month.

Conclusions

Results highlight that students involved in bullying are more vulnerable to NSSI and to psychological symptoms compared to their peers who are not involved in bullying. It is suggested that bullying roles, especially bully-victim status, need to be identified in school and online settings and thus special attention should be addressed to them to reduce psychological symptoms and NSSI, for example by enhancing adaptive coping skills.

Peer Review reports

Introduction

As nonsuicidal self-injury (NSSI) – the intentional, direct destruction (e.g., scratching, bruising, cutting, burning, biting) of one’s own body tissue without suicidal intent [ 1 ] – is a considerable behavioral problem, especially among adolescents [ 2 , 3 ], and has become an even more widespread phenomenon throughout the last decade [ 4 , 5 ], research should focus more on this topic. Some evidence indicates that NSSI can be a stronger predictor of suicide attempts than previous suicidal behavior [ 6 , 7 ]. NSSI typically occurs in early adolescence (between the age of 12–14) [ 5 , 8 ] and peaks in mid-adolescence [ 9 , 10 ]. The prevalence can be remarkably high in community youth samples (between 14.5 and 46.5%) [ 11 , 12 , 13 ], and based on meta-analytical results, females are more at risk for NSSI [ 14 ].

The current study focuses on three phenomena typically occuring during adolescence and thus may also be the cause of difficulties in school settings: along with NSSI , we also focus on bullying , and internalizing and externalizing problems. While previous research mainly focuses on bullying victimization and victims, by differentiating the roles of bullying (bully, victim, bully-victim), the aim of this study is to investigate whether bullies and bully-victims are also vulnerable to mental health issues (NSSI, internalizing and externalizing problems). Furthermore, another intention of this study is to establish and test mediation models that can be taken into consideration when planning NSSI and mental health related interventions in school settings. Our research includes cyberbullying as well, which is less investigated as school-based bullying but more and more a widely spread type of peer aggression.

Bullying and bullying roles

In the current study, bullying is defined as a type of youth violence which includes any unwanted aggressive behavior by another youth or group of youths who are not siblings or current dating partners [ 15 , 16 ]. Bullying can take place in the school or in any online platforms, the latter is cyberbullying [ 17 , 18 ]. In our study, a traditional classification of bullying roles was applied: perpetrator, victim, and bully-victim [ 19 , 20 ]. Bully/perpetrator is the person who commits the bullying and have a perceived dominance or more power than the victim. Victim is a person who suffers from being bullied and perceived as less dominant or having less power. B ully-victim s are those who are both victims and perpetrators [ 19 , 20 , 21 ].

Regarding bullying roles, gender differences can be observed: boys are more likely to engage in bullying others in school and in online settings [ 22 , 23 ], while girls are more likely to be the victims of online bullying [ 24 ]. Based on the data of the HBSC (Health Behavior in School-aged Children) study obtained in 2017/18, the prevalence of bullying perpetration and victimization shows a great variety across the 45 participating countries (the prevalence of perpetration varied from 0.3 to 30% in the 11–15-year-old students, and victimization rates ranged from 0.5 to 32%, respectively) [ 25 ]. However, in many countries there is a decline in bullying perpetration rates. Similar to offline bullying, a significant cross-national variety of prevalence regarding cyberbullying is observable (from 0.6 to 31% in cyberbullying others and from 3 to 29% in being cyberbullied) [ 25 ]. Regarding Hungary, according to the latest HBSC data collection, 28.4% of the 11–17-year-old students reported to have been bullied at least once or twice and 27.1% reported that they have bullied someone in school at least once or twice in the recent three months [ 26 ]. The rates of cyberbullying were lower: 17.8% of students have been cyberbullied and 12.7% of students bullied others online [ 26 ].

The link between bullying roles and externalizing and internalizing problems

Based on different roles in traditional school-based bullying, previous research differentiates connected mental health problems in adolescents. Most of the studies report that bullying perpetrators are more likely to face externalizing problems, while victims are more likely to face internalizing problems [ 23 , 27 , 28 ]. However, the perpetrator-externalizing and victim-internalizing associations can be oversimplifying based on Cook and his colleagues [ 29 ] findings who included online bullying as well. In their meta-analytical results, internalizing problems can be associated with the bully role as well (effect size = 0.12), but the association is stronger for the victims (effect size = 0.25). And similarly, externalizing problems are significantly associated with the victim role (effect size = 0.12), but the association is stronger for bullies, making externalizing problem behaviors the strongest individual predictor of being a bully (effect size = 0.34). Bully-victim role was associated with both externalizing (effect size = 0.33) and internalizing (effect size = 0.22) problems [ 29 ]. Although, previous studies have demonstrated that being a bully-victim in traditional school-based bullying [ 22 , 30 , 31 , 32 ] or in cyberbullying [ 33 , 34 ] might be associated with worse mental health outcomes than either bullies or victims, only a few studies investigated the characteristics and mental health problems of this vulnerable group [ 25 ].

The link between bullying and NSSI

Several risk factors of NSSI have been suggested in previous research (e.g., emotion regulation problems, impulsivity, depressive symptoms), that are mainly individual characteristics [ 35 ]. Less interest has been given to school and peer factors, although the climate of peer relationships or related negative life events can also play an important role in the development of NSSI, furthermore, as it has been recently suggested, academic-related stress and peer bullying is associated with NSSI behaviors [ 36 ], therefore investigating bullying in association with NSSI is essential.

Many cross-sectional studies suggest that adolescents who reported being victims of bullying were at an increased risk for NSSI compared to adolescents who were not victims of bullying or who reported low levels of victimization [ 37 , 38 , 39 ]. Based on two recent studies, involvement in bullying increases the likelihood to engage in NSSI [ 40 , 41 ]. Meta-analytical findings also suggest that bullying is associated with NSSI [ 14 , 42 ]. However, not many studies focused on perpetrators or bully-victims, the associations with NSSI of these roles or the trajectories through which perpetrators or bully-victims are linked to NSSI. Some results show that bullies also engage in NSSI [ 43 ], especially when they had a history of being bullied (which could have made them bully-victims) [ 22 , 44 ]. Additionally, bully-victim girls scored the highest in NSSI [ 44 ] compared to any other role of bullying, and NSSI has also been linked to cyberbullying [ 45 ]. Only a very limited number of research has focused on the association between NSSI and cyberbullying, and these studies mainly investigated cyberbullying victimization. Recent research however shows a higher frequency of NSSI among students involved in cyberbullying and shows that cyberbullying can be in direct association with NSSI [ 12 , 46 , 47 ]. Although online and school-based bullying share common features (e.g., bullying roles, association with mental health problems) remarkable differences occur as well (e.g., higher anonymity, fewer intervention opportunities for affected students, loneliness, role of internet safety features) [ 18 ]. Being alone in online settings might facilitate the appearance of NSSI as feeling lonely is associated with NSSI [ 48 , 49 ] and NSSI happens most often when adolescents are alone [ 50 ]. Furthermore, compared to school-based bullying, online bullying might be more difficult to deal with for the environment (e.g., parents, educators) [ 51 ], making it more difficult for the child to cope with it in the lack of adequate help from significant ones which can also result in a maladaptive coping strategy (e.g., NSSI). A longitudinal study found that cyberbullying can cause harm above and beyond traditional bullying [ 52 ]. Therefore, regarding the remarkable differences between cyberbullying and school-based bullying, it is essential to be able to compare whether bullying in different settings have the same association to NSSI or not. In the current study, online and school-based bullying roles are tested with different mediation models.

Although, most of the previously mentioned findings are cross sectional, some longitudinal cohort and case control studies [ 14 , 44 ] suggest that bullying is not only associated to NSSI but may also predict it.

The link between externalizing and internalizing problems and NSSI

NSSI is often considered as a transdiagnostic element in psychopathology, therefore NSSI-related variables (e.g., suicidality or impulse control difficulties) are best predicted by transdiagnostic variables [ 53 ]. NSSI episodes are prevalent in different psychiatric disorders and psychological symptoms during adolescence (e.g., depression, psychotic symptoms, substance abuse, borderline personality-disorder features, conduct problems, emotional problems) [ 54 , 55 ] and has been linked to externalizing problems (e.g., attention deficit hyperactivity disorder, conduct disorder, oppositional defiant disorder) [ 56 ], and to internalizing problems as well in adolescence [ 57 ]. Some findings suggest that externalizing and internalizing psychopathology are not only associated, but longitudinally predict NSSI [ 58 ].

Externalizing and internalizing problems as possible mediators

Bullying victimization and perpetration can result in interpersonal difficulties or negative interpersonal life events that can cause stress, negative emotions, and mental health problems in adolescence [ 59 , 60 , 61 , 62 , 63 , 64 ]. Subsequently, mental health problems and interpersonal difficulties can trigger and might result in NSSI, thus, they occur comorbidly [ 65 , 66 ]. To cope with negative emotions, NSSI might appear as a dysfunctional emotion regulation [ 3 , 67 , 68 ].

As described in the vulnerability-stress model, suggested by Hankin and Abela [ 69 ], internalizing and externalizing problems are rooted in both individual factors (cognitive vulnerabilities) and in environmental factors (stressors, negative life events, adversities). Environmental factors can be adverse life events that might strengthen the possibility of the development of mental health problems, psychopathology and NSSI in adolescents [ 32 , 70 ]. Bullying might be a significant environmental factor as throughout adolescence the importance of peer relationships and their opinion on oneself can considerably increase the negative influence on the quality of mental health [ 71 , 72 ]. Few mediator models have been established to explain the relationship between bullying and NSSI. Researchers so far have found a partial mediation regarding negative emotions [ 45 ], depressive mood and depressive symptoms [ 22 , 37 ]. In the current study, the possible mediating effect of internalizing and externalizing problems on the relationship between bullying and NSSI among community adolescents was hypothesized and tested.

Aim of the study

The global aim of the current study is to investigate the relationship of school and online bullying, externalizing and internalizing problems, and NSSI.

By establishing different bullying roles, this study seeks answers to the question whether those students who are involved in bullying suffer from greater mental health problems compared to students who are not involved in bullying. The following hypotheses were established:

Adolescents involved in (school or online) bullying show significantly higher internalizing and externalizing scores compared to not involved peers. It is also hypothesized that bully-victims score significantly higher in internalizing and externalizing problems compared to any other peer group (bullies, victims, not involved adolescents).

Adolescents involved in (school or online) bullying show significantly more serious NSSI behavior (in terms of the number of NSSI episodes and the number of NSSI methods) compared to not involved peers. It is also hypothesized that bully-victims show significantly more serious NSSI behavior compared to any other peer group (bullies, victims, not involved adolescents).

Considering that externalizing and internalizing problems are both correlated to bullying [ 23 ] and NSSI [ 65 ], the current study establishes six mediation models (Figs.  1 , 2 , 3 , 4 , 5 and 6 ) to understand the relationship among different school and online bullying roles, externalizing and internalizing problems, and NSSI. Accordingly, the following hypotheses were examined:

The association of school and online bullying roles with NSSI will be partially mediated by both externalizing and internalizing symptoms (Figs.  1 , 2 , 3 and 4 ) given that both bullying perpetration and victimization might be related to internalizing and externalizing problems [ 29 ];

The association of the frequency of school victimization and NSSI will be partially mediated by externalizing and internalizing problems (Figs.  5 and 6 , respectively).

Gender and age are taken into consideration as control variables in the mediation models. As NSSI is predicted by various factors, it was hypothesized that externalizing and internalizing problems would only decrease the direct effect of bullying on NSSI rather than eliminate the association. The study also seeks answer to the question whether bully-victims are in need of intervention due to higher mental health problems (i.e., NSSI, externalizing and internalizing problems) compared to victims and bullies and not involved students.

Hypothesized mediation model 1

Hypothesized mediation model 2

Hypothesized mediation model 3

Hypothesized mediation model 4

Materials and methods

Participants and procedure.

This cross-sectional study involved 14 Hungarian secondary schools from the capital city and from the countryside. Specific schools were asked to participate based on accessibility to the researchers while following the idea to represent different type of secondary schools based on location (e.g., metropolitan area, smaller cities) and on educational profile (e.g., high-school, vocation schools). Data collection started in February 2019 and finished in January 2020. Participants were from all grades (grades 9–12) of secondary schools. During data collection, trained investigators were present, but not any teachers. Students filled out the questionnaires either in their classroom (paper-based questionnaires) or online (on the Qualtrics platform) in computer rooms or on smart phones according to the circumstances of the schools. Online questionnaires were filled out in the classroom, in-person settings as well (e.g., during informatics class) in the presence of trained investigators.

More than one thousand and two hundred students (N = 1232) were requested to take part in the study and a total of 1059 students agreed in participating. 173 students were either absent during data collection or declined to participate. From the 1059 who agreed in the participation, 48 were excluded due to incomplete answers. Thus, the final sample consists of 1011 students, mostly females (n = 668; 66.07% girls), the mean age came to 16.81 (SD = 1.41) years. The youngest participants were 14 years old; the oldest participants were 20 years old. Most of the participants live in cities (n = 450, 44.5%) or in the capital (n = 252; 24.9%), while 309 (30.6%) students live in villages.

All aspects of the study were ethically approved by the Institutional Review Board of ELTE Eötvös Loránd University, Budapest, Hungary. Participation was voluntary and anonymous. Students and one of their parents had to give their written consent to participate in the research, while headmasters of secondary schools were also informed about the details of the study and gave their consent to carry out the research in their institution. The Declaration of Helsinki [ 73 ] was taken into account while carrying out the research. An information sheet about the meaning, characteristics of NSSI and possible sources (online and in person) of help was provided to every participant.

Inventory of Statements About Self-injury

NSSI is often measured with the Inventory of Statements About Self-Injury (ISAS) [ 74 ]. In this study, Hungarian version of the short form was used [ 75 ] (Hungarian version: [ 76 ]). The short form of ISAS has two parts, the first assesses prevalence, types (12 different – plus one free answer – NSSI behaviors, e.g., cutting, biting, carving, severe scratching or hitting self) and characteristics of NSSI (e.g., age of onset, the experience of pain during NSSI, whether NSSI is performed alone or around others). The second part measures 13 functions of NSSI [ 74 ]. In the current study, only frequency, methods of NSSI and time of the last episode were analyzed from the first part of the ISAS. At the beginning of the questionnaire, definition of NSSI was given, underlying the importance of the act being deliberate without suicidal intent.

The Hungarian version of the Revised Olweus Bully/Victim Questionnaire [ 77 ] was used to measure bullying which was used in the Hungarian HBSC Study [ 26 ]. First, a precise definition was given to participants about the meaning of bullying: “We say a student is being bullied when another student, or several other students say mean and hurtful things to him or her; or make fun of him or her; or call him or her mean and hurtful names; or completely ignore or exclude him or her from their group of friends or leave him or her out of things on purpose. When we talk about bullying, these things happen repeatedly, and it is difficult for the student being bullied to defend himself or herself. We also call it bullying, when a student is teased repeatedly in a mean and hurtful way. But we don’t call it bullying when the teasing is done in a friendly and playful way. Also, it is not bullying when two students of about equal strength or power argue or fight.” In the first part, school bullying was measured with two questions: the first measures the frequency of bully perpetration during the past few months, while the second asks about the frequency of bully victimization during the same period of time. Online bullying was measured in the same way. The definition of online bullying was also given before the questions, containing examples as well (e.g., sending offensive messages to someone via SMS, chat programs or e-mail, posting such message on someone’s wall on social media).

Based on the previous questions, four different roles of school and online bullying were differentiated: school bullies were participants who at least once or twice have bullied someone else at school during the previous months, but they have not been bullied at all at school. Online bullies were participants who at least once or twice have bullied someone else online during the previous months, but they have not been bullied online. School victims were those participants who have been bullied at least once or twice during the previous months at school, but they have not bullied others at all in school settings. Online victims were those participants who have been bullied at least once or twice during the previous months on online platforms, but they have not bullied others at all online. School bully-victims are those participants who have bullied others at least once or twice at school in the previous months and who have been bullied as well at least once or twice at school during the previous months. Online bully-victims are those participants who have bullied others at least once or twice online in the previous months and who have been bullied as well at least once or twice online during the previous months. Not involved students have not bullied others and have not been bullied either, neither in school, nor online during the previous months.

In the second part of the questionnaire, seven different types of school bullying victimization (e.g., being excluded from activities or social groups, being ignored, being mocked) were measured. Items 6 (being mocked because of religion) and 7 (experiencing sexual comments) were developed by the Canadian HBSC group [ 78 ].

Participants could give their answers on a 5-point Likert-scale (1 =  never during the past few months , 2 =  once or twice , 3 =  two or three times a month , 4 =  approximately once a week , 5 =  several times a week ).

In the current study, reliability of the second part of the questionnaire (types of school victimization) was good (α = 0.70). Previous studies did not report reliability data concerning the second part of the Revised Olweus Bully/Victim Questionnaire [ 77 ].

Strengths and Difficulties Questionnaire (SDQ)

The self-report version of the Strengths and Difficulties Questionnaire [ 79 ] (Hungarian translation: [ 80 ]) is a brief emotional and behavioral screening questionnaire for children and young people and is a valid and reliable instrument to measure externalizing and internalizing symptoms in adolescence. Respondents use a 3-point scale to indicate how far each item applies to them (1 =  not true , 2 =  somewhat true , 3 =  completely true ). The 25 items are divided between 5 subscales, with five items: emotional symptoms (e.g., “ I am often unhappy, downhearted or tearful. ”), conduct problems (e.g., “ I fight a lot. I can make other people do what I want.” ), hyperactivity-inattention (e.g., “ I am constantly fidgeting or squirming.” ), peer problems (e.g., “ I am usually on my own. I generally play alone or keep to myself.”) , and prosocial behavior (e.g., “I often volunteer to help others (parents, teachers, children).” ). The authors of the questionnaire suggest the use of a three-subscale division of the SDQ [ 81 ] in low-risk or general population samples: internalizing problems (emotional symptoms + peer problems, 10 items), externalizing problems (conduct problems + hyperactivity symptoms, 10 items) and prosocial scale (5 items). In the current study, only internalizing and externalizing subscales were used. Reliability of the subscales were good in our study (internalizing subscale α = 0.75 was better than the original’s α = 0.66; externalizing subscale α = 0.76 was similar to the one in the original study α = 0.76) [ 81 ].

Data analysis

Basic characteristics of the sample, descriptive statistics of the variables and correlations were performed in IBM SPSS 28, the level of significance was taken as 0.05. Mediation analyses were performed in Mplus 8.0 [ 82 ].

NSSI was analyzed via two binary variables in the mediation models: one consists of no history of NSSI and past NSSI (at least one NSSI episode in the past, earlier than a month), while the other consists of no history of NSSI and current NSSI (at least one NSSI episode in the last month). In the ANOVA analysis, NSSI was a categorical variable with three values: no history of NSSI, past NSSI and current NSSI.

Two variables measured the severity of NSSI. Number of NSSI methods was a continuous variable, while number of NSSI episodes was a binary variable (non-repetitive NSSI = 1–9 episodes, repetitive NSSI = 10 or more episodes based on the suggestion of Gratz et al., [ 83 ]).

In the case of bullying, three different bullying variables were analyzed: (1) school bullying roles (1 = victim, 2 = bully, 3 = bully-victim, 4 = not involved in bullying) as a categorical variable; (2) online bullying roles (categories are the same as at school bullying); and (3) frequency of different school victimization types (higher score means more frequent school bullying victimization; used as a continuous variable).

Victim status was measured in two ways: in the first part of the questionnaire, the frequency of bullying victimization was asked in general (“ How often were you bullied during the past months?” ), while in the second part concrete items measure the frequency of different school bullying types.

As suggested in the literature [ 84 ], dummy variables were created for independent categorical variables (school and online bullying roles, see categories in the Measures part) to use in mediation modeling. Externalizing and internalizing problems were continuous variables, higher scores mean stronger internalizing and externalizing problems.

Pearson correlations were performed to measure associations between different variables. Group differences (NSSI, school bullying roles, online bullying roles) regarding externalizing and internalizing problems, and NSSI severity (number of NSSI methods) were assessed with one-way ANOVA. Crosstabulation was performed regarding the number of NSSI episodes (binary variable). In the case of NSSI severity (number of NSSI methods and number of NSSI episodes) normal distribution was violated, therefore the robust version of ANOVA (Welch test) was used. When Levene’s test claimed the violation of the homogeneity of variances, a robust post-hoc test (Games-Howell) was used, otherwise, the results of non-robust post-hoc test (Tukey) are reported. Post-hoc tests are used to compare group differences. To avoid type 1 error, p-values were adjusted during post-hoc analyses in the following way: when analyzing NSSI severity and internalizing and externalizing symptoms in different school and online bullying groups, 4 groups were compared with each other that resulted in 6 comparisons (not involved students vs. bully-victims; not involved students vs. bullies; not involved students vs. victims; bully-victims vs. bullies; bully-victims vs. victims; bullies vs. victims), therefore, p value of 0.05 was divided by 6 which results in p < .0083. Comparing three different NSSI groups in regards with internalizing and externalizing symptoms, three comparisons were made (no NSSI vs. past NSSI; no NSSI vs. current NSSI; past NSSI vs. current NSSI) therefore p value of 0.05 was divided by 3 which results in p < .016. In the Results, only results significant according to the adjusted level are reported. In the comparisons of groups, reference group was the not involved students in case of bullying roles. Regarding NSSI, reference was the no NSSI group (students who have never engaged in NSSI).

Six mediation models were established based on the literature and tested in the current study via Structural Equation Modeling. Mediator variables in every model were both externalizing and internalizing problems. Due to low correlation between the two mediator variables (r = .27), they were tested parallelly within the same models. Gender (1 = boys, 2 = girls) and age (continuous variable) were control variables in each mediation model.

In the models, observed variables were school bullying roles (Model 1, Fig.  1 ), online bullying roles (Model 2, Fig.  2 ) and frequency of school victimization (Model 3, Fig.  3 ). Outcome variables were No NSSI/Past NSSI and No NSSI/Current NSSI. Due to categorical variables, MLR (robust version of maximum likelihood parameter) estimator was used to perform Structural Equation Modeling [ 85 ]. Models are saturated as every possible connection is coded in the models.

Hypothesized mediation model 5

Hypothesized mediation model 6

Characteristics of NSSI

Almost one third (n = 320, 31.7%) of the current sample engaged in NSSI at least once within the last month. Prevalence of past NSSI was 9.4% (n = 95; participants who had engaged in NSSI some point in their life but did not engage within the last month). Among those who have ever engaged in NSSI, the most common methods were banging or hitting oneself (n = 222; 53.1%), interfering with wound healing (n = 218; 52.2%), cutting (n = 170; 40.7%), biting (n = 163; 39%), pinching (n = 162; 38.8%) and severe scratching (n = 144; 34.4%). Mean age of the first NSSI episode was 11.99 years (SD = 3.52). Significantly more girls (n = 293; 43.86%) engaged in NSSI than boys (n = 121; 35.27%; χ 2 (1) = 32.40; p < .001). The highest number of used NSSI methods was 11 from 13. Among those who have engaged in NSSI, 3.80 (SD = 2.69) methods were used in average. Mean score for number of NSSI episodes was 111.91 (SD = 935.96). Lifetime repetitive NSSI (≥ 10 episodes based on the suggestion of Gratz [ 83 ]) was 72.05% (n = 299) of those who have ever engaged in NSSI.

Descriptive statistics and correlation of the variables

Descriptive statistics and correlation of continuous variables, gender and age are shown in Table  1 . Female gender was associated to increased internalizing problems and to number of NSSI methods. Frequency of different school victimization types was slightly associated to lower age, while higher level of internalizing problems was associated to being older. School victimization was almost equally associated to internalizing and externalizing problems. Higher number of used NSSI methods was associated to a higher level of internalizing, externalizing and to more frequent school victimization.

Table  2 shows the crosstabulation of number of participants involved in different roles of school and online bullying. More girls were victims of school bullying (χ 2 (1) = 6.12; p < .01), while significantly more boys were bullies (χ 2 (1) = 20.92; p < .01) and bully-victims (χ 2 (1) = 4.15; p < .05), compared to girls. Similar gender differences emerged in cyberbullying with higher number of female victims (χ 2 (1) = 4.62; p < .05), higher number of male bullies (χ 2 (1) = 10.06; p < .01), and bully-victims (χ 2 (1) = 8.39; p < .01). Most frequent school victimization types were being excluded from activities, social groups or being ignored (n = 344; 33.9%); spreading rumors or fake news (n = 292; 28.8%); being calling names and teasing or made fun of (n = 249; 24.5%) and being the target of sexual comments (n = 120; 11.8%). Those who have ever been or currently are involved in NSSI reported higher frequency of school victimization (M = 9.44; SD = 3.21) compared to peers who are not involved in NSSI (M = 8.24; SD = 2.25; t = 6.53; p < .001). The number of participants who were bullied at school in any form at least once or twice (n = 568) is considerably higher compared to those who reported being bullied at school (n = 91) or online (n = 93) when asking simply how frequently the bullying happened (no concrete types of bullying were given).

Group differences in the severity of NSSI

Welch test revealed that different school-related bullying roles significantly differ in the number of NSSI methods (F (3;139.62) = 13.36; p < .001). Table  3 shows post hoc analysis and group differences. Those who have participated in school bullying in any form (bully, victim, bully-victim) use significantly more NSSI methods compared to their peers who have not participated in bullying at all. Similar results emerged in case of cyberbullying.

A crosstabulation in Table  4 shows the rate of repetitive NSSI and non-repetitive NSSI in different bullying roles.

Group differences in externalizing and internalizing problems

One-way ANOVA revealed that NSSI groups significantly differ in both the level of externalizing problems (F(2,1007) = 26.11; p < .001) and internalizing problems (F(2,1007) = 62.28; p < .001) as well. Table  5 contains post hoc comparisons of different groups. Regarding externalizing symptoms, the mean score of no-NSSI group was significantly lower than the mean score of past NSSI group and current NSSI group. Internalizing problems showed the same pattern.

School and online bullying roles differed significantly in the level of externalizing problems (school bullying: F(3,1001) = 15.93; p < .001; online bullying: F(3,1003) = 17.88; p < .001) and in the level of internalizing problems as well (school bullying: F(3,1001) = 30.38; p < .001; online bullying: (F(3,1003) = 18.65; p < .001). Post hoc test revealed that every school bullying role showed significantly higher level of externalizing problems than those who were not involved in school bullying, with the highest average score of bully-victims and bullies.School bullying roles differed significantly in the level of internalizing problems as well. Compared to those who were not involved in bullying, victims and bully-victims had significantly higher scores of internalizing problems, but bullies did not differ significantly. Victims and bully-victims scored significantly higher on internalizing symptoms compared to bullies, but there was no significant difference between victims and bully-victims. Online bullying roles differed significantly in the level of externalizing problems. Post hoc test revealed that online bullies and online bully-victims scored significantly higher on externalizing problems than those who were not involved in online bullying.

Online bullying roles differed significantly in the level of internalizing problems as well. Post hoc test revealed that online victims and online bully-victims scored significantly higher on internalizing problems than those who were not involved in online bullying; regarding bullies there was no difference compared to the reference group.

Mediation analysis

All standardized regression coefficients and standard errors of total, direct, and indirect effects related to each model are detailed in the Supplementary Materials Table S1 . While Table S2 in the Supplementary Materials contains odds ratios and confidence intervals.

In Model 1, explained variance of past NSSI was 14.8% (Table S1 , Table S2 , Fig.  7 ). Despite significant associations in the model, only two significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 1.4. victim – internalizing problems – past NSSI (β = 0.052; SE = 0.02; p < .01); path 1.6. bully-victim – internalizing problems – past NSSI (β = 0.036; SE = 0.01; p < .01) (Table S1 , Table S2 ).

Final Model 1 of school bullying and No/Past NSSI showing standardized coefficients

Note: Significant paths are marked with bold numbers and arrows. Sch_victim = school bullying victim; Sch_bully = school bullying perpetrator/bully; Sch_b-v = school bullying bully-victim; *p < .05; **p < .01; ***p < .001

In Model 2, explained variance of current NSSI was 18.9% (Table S1 , Table S2 , Fig.  8 ). Five significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 2.1. bully – externalizing problems – current NSSI (β = 0.022; SE = 0.01; p < .01); path 2.3. victim – externalizing problems – current NSSI (β = 0.014; SE = 0.01; p < .05); path 2.4. victim – internalizing problems – current NSSI (β = 0.072; SE = 0.01; p < .001); path 2.5. bully-victim – externalizing problems – current NSSI (β = 0.027; SE = 0.01; p < .01); path 2.6. bully-victim – internalizing problems – current NSSI (β = 0.050; SE = 0.01; p < .001) (Table S1 , Table S2 ).

Final Model 2 of school bullying and No/Current NSSI showing standardized coefficients and standard errors

In Model 3, explained variance of past NSSI was 13.6% (Table S1 , Table S2 , Fig.  9 ). Two significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 3.4. victim – internalizing problems – past NSSI (β = 0.044; SE = 0.01; p < .01); 3.6. bully-victim – internalizing problems – past NSSI (β = 0.032; SE = 0.01; p < .01) (Table S1 , Table S2 ).

Final Model 3 of online bullying and No/Past NSSI showing standardized coefficients and standard errors

Note: Significant paths are marked with bold numbers and arrows. Onl_victim = online bullying victim; Onl_bully = online bullying perpetrator; Onl_b-v = online bullying bully-victim; *p < .05; **p < .01; ***p < .001

In model 4, explained variance of current NSSI was 19.0% (Table S1 , Table S2 , Fig.  10 ). Five significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 4.1. bully – externalizing problems – current NSSI (β = 0.022; SE = 0.01; p < .01); path 4.3. victim – externalizing problems – current NSSI (β = 0.011; SE = 0.01; p < .05); path 4.4. victim – internalizing problems – current NSSI (β = 0.058; SE = 0.04; p < .001); path 4.5. bully-victim – externalizing problems – current NSSI (β = 0.028; SE = 0.01; p < .01); path 4.6. bully-victim – internalizing problems – current NSSI (β = 0.043; SE = 0.01; p < .001) (Table S1 , Table S2 ).

Final Model 4 of online bullying and No/Current NSSI showing standardized coefficients and standard errors

In Model 5, explained variance of past NSSI was 14.1% (Table S1 , Table S2 , Fig.  11 ). One significant indirect effect size estimate was presented in this mediation model, suggesting a significant mediated pathway: path 5.2. school victimization – internalizing problems – past NSSI (β = 0.062; SE = 0.02; p < .01) (Table S1 , Table S2 ).

Final Model 5 of school victimization and No/Past NSSI showing standardized coefficients and standard errors

Note: Significant paths are marked with bold numbers and arrows. *p < .05; **p < .01; ***p < .001

In Model 6, explained variance of current NSSI was 18.7% (Fig.  12 ). Two significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path (6.1) school victimization – externalizing problems – current NSSI (β = 0.044; SE = 0.01; p < .001); path (6.2) school victimization – internalizing problems – current NSSI (β = 0.090; SE = 0.04; p < .001) (Table S1 , Table S2 ).

Final Model 6 of school victimization and No/Current NSSI showing standardized coefficients and standard errors

Our results indicate that mental health problems and NSSI are significantly more relevant for students who are involved in any form of bullying, either in school or online settings, although differences can be detected between various bullying roles. Internalizing and externalizing problems were significant mediators between different bullying roles and current NSSI, although not in the case of NSSI that occurred in the past. School and online bullying did not differ in significant mediation paths.

Our results show a high level of lifetime NSSI (41.17%) in a high-school student sample, which is similar to the latest NSSI research findings [ 12 ].

In our study, we found that more students were involved in traditional bullying than in cyberbullying (23.35% of the students were involved in traditional, school-based bullying, and 16.84% of children were involved in cyberbullying) which is in accordance with similar studies [ 86 ]. The greatest difference in the number of students involved in different roles occurs among online (3.05%) and school (7.98%) bullies. It might be because students do not consider their acts as harmful in the online space and the feedback of the victim is not as direct or visible as in a face-to face situation.

Gender differences we found were in line with previously reported results [ 20 , 22 ]: more girls were victims, more boys were bullies and bully-victims compared to girls, both in school and in online settings. Feijóo and her colleagues [ 87 ] – measuring school victim status in two ways – found that boys suffered more physical violence, were insulted, called names and were threatened, while girls were victims of more relational bullying behaviors (e.g., were excluded or ignored; had rumors spread about them).

Our results show that when measuring concrete types of school victimization, remarkably more students report being victims compared to when only the frequency of being a victim is measured without specific types of bullying listed. It raises attention on the possible phenomenon that high-school students might not be familiar with the terms of assault and victimization. It is also possible that they are not aware of the fact that certain behaviors towards them in school settings are considered as intentional harm doing or aggression. This result is a valuable information for teachers and scientists: it suggests that students might not be aware of the concept of bullying or that different harmful acts should be considered peer violence.

In accordance with meta-analytical findings [ 40 ], victims, bullies and bully-victims were more likely to engage in NSSI than their peers who were not involved in bullying. Students involved in NSSI report more frequent school victimization compared to peers not involved in bullying. Furthermore, those who were not involved in any role of bullying (neither at school or online) reported using significantly less NSSI methods compared to involved participants. This can be interpreted by using the interpersonal theory of NSSI [ 88 ], which considers NSSI as a negative coping strategy, aimed to reduce the stress caused by negative interpersonal events, such as bullying. Bullying is an adverse interpersonal event as a victim, but also as a perpetrator [ 40 ]. Furthermore, the General Strain Theory [ 89 ] suggests that bullying can be experienced as an unjust act that can be resolved with an aggressive behavior. From the victim’s perspective, aggression towards oneself might be the only available option, thus self-harm can be perceived as a temporarily effective way to manage one’s own stress [ 40 ].

Students who are involved in school or online bullying use more NSSI methods than not involved peers, and among them, bully-victims use the most. The number of NSSI episodes (e.g., how often it occurs) in our study did not differentiate between students who are involved and who are not involved in bullying. Although many articles use the frequency of NSSI episodes as an indicator of NSSI severity [ 57 ], it is suggested that the number of NSSI methods predict severity more significantly. NSSI frequency and the number of used methods can also interact, defining a subgroup of individuals seriously at risk [ 90 ]. Robinson and colleagues [ 91 ] found in a community adolescent sample that among adolescents with a lifetime history of NSSI, the number of NSSI methods was strongly associated with reporting suicidal thoughts and behaviors while the number of NSSI episodes was not.

Those who currently engage in NSSI seem to be vulnerable for both externalizing and internalizing problems. Bully-victims reported the highest level of externalizing symptoms in school and online settings, while victims’ level of internalizing problems was the highest both in school and online settings. Higher levels of internalizing problems were present in bully-victims as well. Internalizing symptoms are often conceptualized as significant negative longitudinal outcomes of bullying victimization [ 92 ], however this longitudinal association seem to be bidirectional [ 93 ]. Peer victimization – considered as a significant stressor – can result in internalizing symptoms in adolescents who tend to interpret stressful events in a self-critical manner [ 32 ]. Furthermore, internalizing problems might increase the risk of becoming a target of peer victimization due to individual vulnerabilities (e.g., social withdrawal, avoidance, fearfulness) [ 94 ]. A result that might raise attention on the possible differences of the nature of online and school bullying is that bullies and bully-victims reported higher level of externalizing problems in online settings than in school settings. Online bully-victims also reported significantly higher levels of externalizing symptoms than online victims, a difference, which was not present in school settings. A possible explanation of this might be the online disinhibition phenomenon [ 95 ], that suggests that in online settings users tend to lose their normal capacity of inhibition, partly or completely, as there is no fear of rejection or judgement [ 96 ]. Online bullying might also be a more impulsive act, as the perpetrator has no personal connection with the victim, no facial expression of the victim’s emotions is available and no acquaintance, previous personal contact or physical imbalance is needed [ 86 , 97 ].

In our study we found that, externalizing and internalizing symptoms are more present in students involved in any role of bullying compared to their not involved peers, but different roles seem to be associated differently to symptoms. The differences were not always significant between bullying roles: the results suggest that bully-victims are the most vulnerable group in school and online bullying regarding mental health problems, both in externalizing and internalizing problems. It might be because bully-victims are rejected and isolated by peers and at the same time they are influenced negatively (e.g., to engage in fights) by those adolescents they are friends with [ 29 ]. This suggests that contextual predictors (e.g., peer status and peer influence) can be essential to deal with the bully-victim status. In accordance with Cook’s [ 29 ] meta-analytical findings our results suggest that a bully is possibly an adolescent with significant externalizing behaviors, and also having internalizing symptoms. A victim is an adolescent showing major internalizing symptoms but also engaging in externalizing behaviors to some extent. A bully-victim possibly has comorbid externalizing and internalizing problems which can further worsen his or her mental health.

Models including current NSSI show slightly higher explained variances than those investigating past NSSI. Regarding the mediation models, our main question was whether externalizing and internalizing symptoms mediate the association between different school and online bullying roles and current and past NSSI. Based on indirect effects, results show diverse mediation patterns with specific paths identified regarding different bullying roles.

When NSSI occurred in the past but not currently, both online and school victim and bully-victim roles were significantly associated to NSSI via internalizing problems. The results also underline that school and online victim roles are more strongly associated to internalizing problems and suggest that bully-victims might have comorbid internalizing and externalizing symptoms. Only internalizing symptoms emerged as significant mediators due to the lack of association between externalizing symptoms and past NSSI that happened at least a month before data collection. Based on our mediation analysis and other results, internalizing symptoms are more strongly associated to NSSI (both past and current) than externalizing problems. Emotional and internalizing disorders show clear conceptual overlap with NSSI, as in emotional disorders, negative emotions are often experienced (e.g., fear, anxiety, sadness), which can possibly be maintained by a maladaptive avoidant or coping strategy, like NSSI [ 98 ]. Although, a systematic review suggests that externalizing pathology is also strongly associated to self-injurious behaviors [ 56 ], the study included a wide range of externalizing problems (e.g., attention deficit hyperactivity disorder, oppositional defiant disorder, intermittent explosive disorder) that our study did not, furthermore they included studies which did not differentiate between nonsuicidal and suicidal self-injury. Some studies found a link between externalizing pathology and NSSI happened in the previous year (e.g., [ 57 ], but in our study, past NSSI could occur any time earlier in life, therefore, developmental aspect might play a role in the association of NSSI and externalizing symptoms. Furthermore, the questionnaire asked about externalizing and internalizing symptoms in the previous 6 months, but past NSSI could have occurred earlier than that.

In models with current NSSI, externalizing and internalizing problems seem to be a considerable and significant mediator at most of the bullying roles both in school and online settings. Only bully role was not associated to current NSSI via internalizing problems, which is in accordance with the study’s previous findings, namely that bully role is strongly associated to externalizing problems. Victim and bully-victim status were both associated to current NSSI via externalizing and internalizing symptoms as well, which suggests that not only bully-victims might show comorbid internalizing and externalizing symptoms [ 29 ], but victims as well. Therefore, future research should put special attention on bully-victims and also on victims to specify which leading symptom(s) might be in direct association to the involvement in bullying. Longitudinal studies can reveal the dynamics of the development of being a bully-victim: whether bully-victims were victimized first (i.e., bullied by others) and then started to bully others, or in the opposite way, whether they were initially bullies who then became victims because others took revenge against them [ 44 , 99 ]. A study found that victims in a bullying episode might use aggressive strategies to cope with the situation that tend to perpetuate and escalate the bullying interaction [ 100 ] and therefore might make them a bully-victim. This might be especially true for victims with a relatively high level of externalization [ 86 ] which can also explain our findings that victim status was associated not only to internalizing but to externalizing symptoms as well. In the bully-victim role, guilt might have a special role as well due to the experiences both as a perpetrator who commits the same acts as were done to the person previously [ 101 ].

The results of the models containing school and online bullying, victim role was confirmed by the last two models, containing the frequency of different school victimization types; frequency of school victimization was associated to past NSSI only via internalizing symptoms, while to current NSSI both types of symptoms emerged as significant paths.

The mediation analysis, the settings of bullying (school or online) did not show differences regarding the significant paths via the mediators, which indicates that, in the association of bullying and NSSI, internalizing and externalizing symptoms do not differentiate between school and online settings. The results also suggest that internalizing and externalizing symptoms should be addressed when NSSI occurs currently in a student’s life. As externalizing and internalizing problems only partly mediate the association between different bullying roles and NSSI, to build a complex model, other factors should be considered as well. Some psychological features had been already identified as mediator variables, like social self-efficacy (an individual’s belief that he or she can effectively carry out social tasks) [ 47 , 102 ], negative emotions [ 45 ], depressive mood and depressive symptoms [ 22 , 37 ]. It is also essential to identify factors that can help to cope with stress due to bullying and therefore prevent NSSI as a possible maladaptive coping strategy. Hay and Meldrum [ 45 ] found that the relationship between bullying victimization and NSSI almost disappeared in those adolescents who experienced supportive parenting practices. The need for evidence-based guidelines to prevent and react to NSSI behaviors within schools had already been articulated [ 103 ] and the current study emphasizes its importance by highlighting that school-related factors, like bullying, is associated to NSSI.

Finally, limitations of this study are considered. A possible limitation of the study is its cross-sectional nature, which does not allow any assumptions, whether bullying or externalizing and internalizing problems are predicting NSSI or not. Another limitation might be the validity of the measurement of different bullying roles. In the current study, when asking the frequency of concrete school victimization types, participants reported a remarkably higher occurrence of school victimization than when asking only the frequency in general. As different bullying roles (victim, bully, bully-victim) were established based on the reported frequency (without asking concrete acts), it is possible that participants would have reported a higher and therefore more valid frequency of bully acts based on different types of bully acts given. This limitation however raises attention to the importance of making awareness of the concept and nature of bullying and peer violence in schools. A relatively high prevalence of bullying might be because one single act of bullying (perpetration, victimization, or both) was enough to fulfill a certain category of bullying role. Regarding bullying roles, another limitation should be the possible clustering effect of students from the same class (students from the same class know each other and spend a lot of time together), that was not controlled in the current study. Future studies using more robust analyses (e.g., multilevel structural equation modeling) are suggested to take care of this statistical issue. The unequal number of male and female participants in this study should be considered a limitation, as gender differences are remarkable in NSSI [ 104 ] and in bullying [ 24 ] as well.

In our study, we applied a traditional way of distinguishing different bullying roles (bully, victim, bully-victim) [ 19 , 20 ] however, according to other perspectives, children could fall along a bully-victim continuum and roles demonstrate a significant overlap [ 105 ]. The results should be interpreted with the approach that due to the possible overlap between different bullying roles that were not taken into consideration in the current study, it is possible that students involved in both online and school bullying but in different roles have different psychological needs and difficulties compared to students being involved in one form of bullying, in one single role. Therefore, in future studies, latent cluster or latent profile analyses should be applied to be able to distinguish these, often co-occurring bullying roles.

Although, the sample size of the current study is adequate to make complex statistical analyses, eight subgroups were formed (school victims, school bullies, school bully-victims, not involved participants in school bullying, online victims, online bullies, online bully-victims, not involved participants in online bullying) from which the group of online bullies contains only n = 31 participants.

Current NSSI seems to be more relevant regarding bullying in our study, but a limitation might be that past NSSI could have occurred any time during life, while bullying roles and psychological symptoms (externalizing and internalizing problems) were measured based on the occurrence during the previous few months, or previous six months, respectively. Finally, regarding that our study focused exclusively on the path through which bullying is linked to NSSI via externalizing and internalizing symptoms, future research should focus on other possible mediator and moderator variables.

Based on the results, students involved in bullying are more vulnerable to NSSI and to psychological symptoms compared to their peers who are not involved in bullying. Externalizing and internalizing problems do significantly mediate the association of different bullying roles and NSSI, but to different extent and through different paths. Psychological symptoms seem to play a significant role when NSSI occurs currently throughout the last month. Bully role seems to be associated firstly to externalizing symptoms, but internalizing problems can be present as well. Victim role seems to be slightly associated to externalizing problems, but internalizing symptoms should be addressed in the first place. At bully-victims, comorbid internalizing and externalizing symptoms might occur, however their engagement in NSSI does not seem to be more severe than victims’ or bully’s engagement. Bullying prevention is important because its connection to NSSI is significant. Inconsistencies regarding the self-report of victim role and different types of victimization raises attention on the importance of raising awareness on the phenomenon of bullying and empowering more vulnerable students to be conscious about being maltreated by peers.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Nonsuicidal self-injury

World Health Organization

Health Behaviour in School-aged Children study

Inventory of Statements About Self-Injury

Strengths and Difficulties Questionnaire

Standard deviation

Klonsky ED, Victor SE, Saffer BY. Nonsuicidal self-injury: what we know, and what we need to know. Can J Psychiatry. 2014;59:565–8.

Article   PubMed   PubMed Central   Google Scholar  

Lloyd-Richardson EE, Perrine N, Dierker L, Kelley ML. Characteristics and functions of non-suicidal self-injury in a community sample of adolescents. Psychol Med. 2007;37(8):1183–92.

Nock MK. Self-Injury. Annu Rev Clin Psychol. 2010;6(1):339–63.

Article   PubMed   Google Scholar  

Cybulski L, Ashcroft DM, Carr MJ, Garg S, Chew-Graham CA, Kapur N, et al. Temporal trends in annual incidence rates for psychiatric disorders and self-harm among children and adolescents in the UK, 2003–2018. BMC Psychiatry. 2021;21(1):229.

Gillies D, Christou MA, Dixon AC, Featherston OJ, Rapti I, Garcia-Anguita A, et al. Prevalence and characteristics of self-harm in adolescents: Meta-analyses of community-based studies 1990–2015. J Am Acad Child Adolesc Psychiatry. 2018;57(10):733–41.

Asarnow JR, Porta G, Spirito A, Emslie G, Clarke G, Wagner KD, et al. Suicide attempts and Nonsuicidal Self-Injury in the treatment of resistant depression in adolescents: findings from the TORDIA Study. J Am Acad Child Adolesc Psychiatry. 2011;50(8):772–81.

Ribeiro JD, Franklin JC, Fox KR, Bentley KH, Kleiman EM, Chang BP, et al. Self-injurious thoughts and behaviors as risk factors for future Suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psychol Med. 2016;46(2):225–36.

Article   CAS   PubMed   Google Scholar  

Nock MK, Prinstein MJ. A Functional Approach to the Assessment of Self-Mutilative Behavior. J Consult Clin Psychol. 2004;72(5):885–90.

Daukantaitė D, Lundh LG, Wångby-Lundh M, Claréus B, Bjärehed J, Zhou Y, et al. What happens to young adults who have engaged in self-injurious behavior as adolescents? A 10-year follow-up. Eur Child Adolesc Psychiatry. 2021;30(3):475–92.

Plener PL, Schumacher TS, Munz LM, Groschwitz RC. The longitudinal course of non-suicidal self-injury and deliberate self-harm: a systematic review of the literature. Borderline Personal Disord Emot Dysregulation. 2015;2(1):2.

Article   Google Scholar  

Cipriano A, Cella S, Cotrufo P. Nonsuicidal Self-injury: a systematic review. Front Psychol. 2017;(8):1–14.

Faura-Garcia J, Orue I, Calvete E. Cyberbullying victimization and nonsuicidal self-injury in adolescents: the role of maladaptive schemas and dispositional mindfulness. Child Abuse Negl. 2021;118:105135.

Lang J, Yao Y. Prevalence of nonsuicidal self-injury in Chinese middle school and high school students. Med. 2018;97(42):e12916.

Wang YJ, Li X, Ng CH, Xu DW, Hu S, Yuan TF. Risk factors for non-suicidal self-injury (NSSI) in adolescents: a meta-analysis. Clin Med. 2022;46:101350.

Google Scholar  

Olweus D. Bully/victim problems among schoolchildren: Basic facts and effects of a school-based intervention program. In: Pepler DJ, Rubin KH, editors. The development and treatmen of child agression. New York: Psychology Press; 1991. pp. 411–46.

Smith PK, Morita Y, Olweus D, Catalano R, Slee P. The nature of School bullying: a cross-national perspective. 1st ed. London: Routledge; 1999.

Bauman S, Cross D, Walker J. Principles of Cyberbullying Research: definitions, measures, and Methodology. 1st ed. London: Routledge; 2012.

Book   Google Scholar  

Olweus D, Limber SP. Some problems with cyberbullying research. Curr Opin Psychol. 2018;19:139–43.

Olweus D. Bullies on the Playground: the role of victimization. In: Craig HH, editor. Children on playgrounds. Research perspectives and applications. 1st ed. New York: New York Press; 1993.

Solberg ME, Olweus D, Endresen IM. Bullies and victims at school: are they the same pupils? Br J Educ Psychol. 2007;77(2):441–64.

Lin YC, Lin CY. Minor symptoms talk: how children react to encountered bullying. Child Indic Res. 2018;11(6):1755–68.

Claes L, Luyckx K, Baetens I, Van de Ven M, Witteman C. Bullying and victimization, depressive Mood, and Non-suicidal Self-Injury in adolescents: the moderating role of parental support. J Child Fam Stud. 2015;24(11):3363–71.

Luukkonen AH, Riala K, Hakko H, Räsänen P, Workgroup S. 70. Bullying behaviour and substance abuse among Underage Psychiatric Inpatient adolescents. Eur Psychiatry. 2010;25(7):382–9.

Smith PK, López-Castro L, Robinson S, Görzig A. Consistency of gender differences in bullying in cross-cultural surveys. Aggress Violent Behav. 2019;45:33–40.

Inchley JC, Stevens GWJM, Samdal O, Currie DB. Enhancing Understanding of Adolescent Health and Well-Being: the Health Behaviour in School-aged children study. J Adolesc Health. 2020;66 Suppl S3-S5.

Németh Á, Várnai D. Kamaszéletmód Magyarországon [ Adolescent life-style in Hungary ]. 1st ed. Budapest: L’Harmattan; 2019.

Eastman M, Foshee V, Ennett S, Sotres-Alvarez D, Reyes HLM, Faris R, et al. Profiles of internalizing and externalizing symptoms associated with bullying victimization. J Adolesc. 2018;65:101–10.

Kelly EV, Newton NC, Stapinski LA, Slade T, Barrett EL, Conrod PJ, et al. Suicidality, internalizing problems and externalizing problems among adolescent bullies, victims and bully-victims. Prev Med. 2015;73:100–5.

Cook CR, Williams KR, Guerra NG, Kim TE, Sadek S. Predictors of bullying and victimization in childhood and adolescence: a meta-analytic investigation. Sch Psychol Q. 2010;25(2):65–83.

Berkowitz R, Benbenishty R. Perceptions of teachers’ support, safety, and absence from school because of fear among victims, bullies, and bully-victims. Am J Orthopsychiatry. 2012;82(1):67–74.

Klomek AB, Kleinman M, Altschuler E, Marrocco F, Amakawa L, Gould MS. High school bullying as a risk for later depression and suicidality. Suicide Life Threat Behav. 2011;41(5):501–16.

Swearer SM, Hymel S. Understanding the psychology of bullying: moving toward a social-ecological diathesis-stress model. Am Psychol. 2015;70(4):344–53.

Kubiszewski V, Fontaine R, Huré K, Rusch E. Cyber-bullying in adolescents: associated psychosocial problems and comparison with school bullying. L’encephale. 2012;39(2):77–84.

Ybarra ML, Mitchell KJ. Online aggressor/targets, aggressors, and targets: a comparison of associated youth characteristics. J Child Psychol Psychiatr. 2004;45(7):1308–16.

Madjar N, Zalsman G, Ben Mordechai TR, Shoval G. Repetitive vs. occasional non-suicidal self-injury and school-related factors among Israeli high school students. Psychiatry Res. 2017;257:358–60.

Evans R, Hurrell C. The role of schools in children and young people’s self-harm and Suicide: systematic review and meta-ethnography of qualitative research. BMC Psychiatry. 2016;14(16):401.

Baiden P, Stewart SL, Fallon B. The mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury among adolescents: findings from community and inpatient mental health settings in Ontario, Canada. Psychiatry Res. 2017;255:238–47.

Alfonso ML, Kaur R. Self-Injury among Early adolescents: identifying segments protected and at Risk*. J Sch Health. 2012;82(12):537–47.

Li X, Chen F, Lin Y, Jia Z, Tucker W, He J, et al. Research on the relationships between psychological problems and school bullying and Non-suicidal Self-Injury among Rural Primary and Middle School students in Developing Areas of China. Int J Environ Res Public Health. 2020;17(10):3371.

Huang H, Ding Y, Wan X, Liang Y, Zhang Y, Lu G, et al. A meta-analysis of the relationship between bullying and non-suicidal self-injury among children and adolescents. Sci Rep. 2022;12(1):17285.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Esposito C, Bacchini D, Affuso G. Adolescent non-suicidal self-injury and its relationships with school bullying and peer rejection. Psychiatry Res. 2019;274:1–6.

Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: a systematic review and meta-analysis. World J Psychiatry. 2017;7(1):60–76.

Serafini G, Aguglia A, Amerio A, Canepa G, Adavastro G, Conigliaro C, et al. The relationship between bullying victimization and perpetration and non-suicidal Self-injury: a systematic review. Child Psychiatry Hum Dev. 2021;54:154–75.

Barker ED, Arseneault L, Brendgen M, Fontaine N, Maughan B. Joint Development of Bullying and victimization in adolescence: relations to Delinquency and Self-Harm. J Am Acad Child Adolesc Psychiatry. 2008;47(9):1030–8.

PubMed   Google Scholar  

Hay C, Meldrum R. Bullying victimization and adolescent Self-Harm: testing hypotheses from general strain theory. J Youth Adolesc. 2010;39(5):446–59.

Wiguna T, Minayati K, Kaligis F, Ismail RI, Wijaya E, Murtani BJ, et al. The effect of cyberbullying, abuse, and screen time on non-suicidal Self-Injury among adolescents during the pandemic: a perspective from the Mediating role of stress. Front Psychiatry. 2021;12:743329.

Zhao H, Gong X, Huebner ES, Yang X, Zhou J. Cyberbullying victimization and nonsuicidal self-injury in adolescents: testing a moderated mediating model of emotion reactivity and dispositional mindfulness. J Affect Disord. 2022;299:256–63.

He N, Xiang Y. Child maltreatment and nonsuicidal self-injury among Chinese adolescents: the mediating effect of psychological resilience and loneliness. Child Youth Serv Rev. 2022;133:106335.

Costa RPDO, Peixoto ALRP, Lucas CCA, Falcão DN, Farias JTDS, Viana LFP, Trindade-Filho EM. Profile of non-suicidal self-injury in adolescents: interface with impulsiveness and loneliness. J Pediatr. 2021;97:184–90.

Reinhardt M, Kökönyei G, Rice KG, Drubina B, Urbán R. Functions of nonsuicidal self-injury in a Hungarian community adolescent sample: a psychometric investigation. BMC Psychiatry. 2021;21(1):1–14.

Kowalski RM, Limber SP, Agatston PW, Cyberbullying. Bullying in the digital age. New Jersey: John Wiley & Sons; 2012.

Englander E, Donnerstein E, Kowalski R, Lin CA, Parti K. Defining cyberbullying. Pediatr. 2017;140(Supplement2):148–S151.

Wang M, Eaton NR. Linking non-suicidal self-injury to psychopathology: the utility of transdiagnostic and DSM-based models. J Affect Disord. 2023;332:55–63.

Muehlenkamp JJ, Brausch AM, Washburn JJ. How much is enough? Examining frequency criteria for NSSI disorder in adolescent inpatients. J Consult Clin Psychol. 2017;85(6):611–9.

Wan YH, Xu SJ, Chen J. Longitudinal effects of psychological symptoms on non-suicidal self-injury: a difference between adolescents and young adults in China. Soc Psychiatry Psychiatr Epidemiol. 2015;50:237–47.

Mészáros G, Horváth LO, Balázs J. Self-injury and externalizing pathology: a systematic literature review. BMC Psychiatry. 2017;17(1):160.

Nock MK, Joiner TE, Gordon KH, Lloyd-Richardson E, Prinstein MJ. Non-suicidal self-injury among adolescents: diagnostic correlates and relation to Suicide attempts. Psychiatry Res. 2006;144(1):65–72.

Fox KR, Franklin JC, Ribeiro JD, Kleiman EM, Bentley KH, Nock MK. Meta-analysis of risk factors for nonsuicidal self-injury. Clin Psychol Rev. 2015;42:156–67.

Lee JY, Kim H, Kim SY, Kim JM, Shin IS, Kim SW. Non-suicidal self-injury is associated with psychotic like experiences, depression, and bullying in Korean adolescents. Early Interv Psychiatry. 2021;15(6):1696–704.

Pengpid S, Peltzer K. Anxiety-induced sleep disturbances among in-school adolescents in the United Arab Emirates: Prevalence and associated factors. Asian J Soc Health Behav. 2022;5:18–23.

Chen JK, Yang B, Wang LC, Chang CW, Lin CY. Is psychological distress a risk factor or an outcome of school Violence and cyberbullying perpetrated by adolescents? A short-term longitudinal panel study. J Interpers Violence. 2023;0(0):1–24.

Chen JK, Chang CW, Lin CY, Wang LC. An investigation of Longitudinal associations between Psychological Distress and Student victimization by teachers. J Interpers Violence. 2023;38(3–4):3279–97.

Chen IH, Gamble JH, Lin CY. Peer victimization’s impact on adolescent school belonging, truancy, and life satisfaction: a cross-cohort international comparison. Curr Psychol. 2023;42(2):1402–19.

Lin YC, Latner JD, Fung XCC, Lin CY. Poor health and experiences of being bullied in adolescents: self-perceived overweight and frustration with appearance matter. Obesity. 2018;26(2):397–404.

Cerutti R, Zuffianò A, Spensieri V. The role of Difficulty in identifying and describing feelings in Non-suicidal Self-Injury Behavior (NSSI): associations with perceived attachment quality, Stressful Life events, and suicidal ideation. Front Psychol. 2018;9:318.

Tang J, Yang W, Ahmed NI, Ma Y, Liu HY, Wang JJ, et al. Stressful life events as a predictor for Nonsuicidal Self-Injury in Southern Chinese Adolescence: a cross-sectional study. Med (Baltim). 2016;95(9):e2637.

Andover MS, Morris BW. Expanding and clarifying the role of emotion regulation in Nonsuicidal Self-Injury. Can J Psychiatry. 2014;59(11):569–75.

Klonsky ED. The functions of deliberate self-injury: a review of the evidence. Clin Psychol Rev. 2007;27(2):226–39.

Hankin BL, Abela JRZ. Depression from Childhood through Adolescence and Adulthood: a developmental vulnerability and stress perspective. In: Hankin BL, Abela JRZ, editors. Development of psychopathology: a vulnerability-stress perspective. 1st ed. Thousand Oaks: Sage Publications Inc; 2005. pp. 245–88.

Chapter   Google Scholar  

Zetterqvist M, Lundh LG, Dahlström Ö, Svedin CG. Prevalence and function of Non-suicidal Self-Injury (NSSI) in a community sample of adolescents, using suggested DSM-5 criteria for a potential NSSI disorder. J Abnorm Child Psychol. 2013;41(5):759–73.

Horváth LO, Győri D, Komáromy D, Mészáros G, Szentiványi D, Balázs J. Nonsuicidal Self-Injury and Suicide: the role of life events in clinical and non-clinical populations of adolescents. Front Psychiatry. 2020;11:370.

Waylen A, Wolke D. Sex ‘n’ Drugs ‘n’ rock ‘n’ roll: the meaning and social consequences of pubertal timing. Eur J Endocrinol. 2004;151 Suppl:U151–9.

World Medical Association. Declaration of Helsinki. Ethical principles for medical research involving human subjects. B World Health Organ. 2003;79(4):373–4.

Klonsky ED, Glenn CR. Assessing the functions of non-suicidal self-injury: psychometric properties of the inventory of statements about self-injury (ISAS). J Psychopathol Behav Assess. 2009;31(3):215–9.

Washburn JJ, Klonsky ED, Styer DM, Gebhardt M, Juzwin KR, Aldridge D et al. Short form of the Inventory of Statements About Self-Injury. Poster presentation at the 7th annual meeting of the International Society for the Study of Self-Injury. Chapel Hill, NC. 2012.

Reinhardt M, Kökönyei G, Drubina B, Urbán R. A nem szuicidális önsértés és motivációjának mérése serdülőknél: Az Állítások Az Önsértés Kapcsán Kérdőív (ISAS-HU) magyar adaptációja. Magy Pszichol Szle. 2022;76(3–4):515–48.

Olweus D. The revised Olweus Bully/Victim questionnaire. Bergen: Research Center Health Promotion; 1996.

Craig W, Harel-Fisch Y, Fogel-Grinvald H, et al. A cross-national profile of bullying and victimization among adolescents in 40 countries. Int J Public Health. 2009;54(Suppl):216–24.

Goodman R, Meltzer H, Bailey V. The strengths and difficulties Questionnaire: a pilot study on the validity of the self-report version. Int Rev Psychiatry. 2003;15(1–2):173–7.

Turi E, Tóth I, Gervai J. A Képességek és Nehézségek Kérdőív (SDQ-Magy) további vizsgálata nem-klinikai mintán, fiatal serdülők körében. [Investigation of the strengths and difficulties Questionnaire in the sample of non-clinical young adolescnets] . Psychiat Hun. 2011;26:415–26.

Goodman A, Lamping DL, Ploubidis GB. When to use broader Internalising and Externalising Subscales instead of the hypothesised five subscales on the strengths and difficulties Questionnaire (SDQ): data from British parents, teachers and children. J Abnorm Child Psychol. 2010;38(8):1179–91.

Muthén LK, Muthén B. Mplus user’s guide: Statistical analysis with latent variables, user’s guide. 8th ed. 2017. https://www.statmodel.com/download/usersguide/MplusUserGuideVer_8.pdf Accessed 18 Apr 2023.

Gratz KL, Dixon-Gordon KL, Chapman AL, Tull MT. Diagnosis and characterization of DSM-5 Nonsuicidal Self-Injury disorder using the Clinician-Administered Nonsuicidal Self-Injury Disorder Index. Assessment. 2015;22(5):527–39.

Bollen KA. Structural equations with latent variables. New York: John Wiley & Sons; 1989.

Muthén LK, Muthén BO, Mplus. Statistical analysis with latent variables user’s guide 6.0. Los Angeles: Muthén & Muthén; 2010.

Smith PK, del Barrio C, Tokunaga R. Definitions of bullying and cyberbullying: how useful are the terms? In: Bauman S, Cross D, Walker J, editors. Principles of cyberbullying research: definition, methods, and measures. New York & London: Routledge; 2012. pp. 64–86.

Feijóo S, O’Higgins-Norman J, Foody M, Pichel R, Braña T, Varela J, et al. Sex differences in adolescent bullying behaviours. Psychosoc Interv. 2021;30(2):95–100.

Hilt LM, Nock MK, Lloyd-Richardson EE, Prinstein MJ. Longitudinal study of Nonsuicidal Self-Injury among Young adolescents: Rates, correlates, and preliminary test of an interpersonal model. J Early Adolesc. 2008;28(3):455–69.

Agnew R. Pressured into crime: an overview of general strain theory. UK: Oxford University Press USA; 2006.

Ammerman BA, Jacobucci R, Turner BJ, Dixon-Gordon KL, McCloskey MS. Quantifying the importance of lifetime frequency versus number of methods in conceptualizing nonsuicidal self-injury severity. Psychol Violence. 2020;10:442–51.

Robinson K, Garisch JA, Wilson MS. Nonsuicidal self-injury thoughts and behavioural characteristics: associations with suicidal thoughts and behaviours among community adolescents. J Affect Disord. 2021;282:1247–54.

Camerini AL, Marciano L, Carrara A, Schulz PJ. Cyberbullying perpetration and victimization among children and adolescents: a systematic review of longitudinal studies. Telemat Inf. 2020;49:10162.

Christina S, Magson NR, Kakar V, Rapee RM. The bidirectional relationships between peer victimization and internalizing problems in school-aged children: an updated systematic review and meta-analysis. Clin Psychol Rev. 2021;85:101979.

Schacter HL, Juvonen J. Depressive symptoms, friend distress, and self-blame: risk factors for adolescent peer victimization. J Appl Dev Psychol. 2017;51:35–43.

Gavidia-Payne S, Denny B, Davis K. Children’s self-concept: parental school engagement and student–teacher relationships in rural and urban Australia. Soc Psychol Educ. 2015;18:121–36.

Lapidot-Lefler N, Barak A. Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition. Comput Hum Behav. 2012;28(2):434–43.

Vandebosch H, Van Cleemput K. Defining cyberbullying: a qualitative research into the perceptions of youngsters. Cyberpsychol Behav. 2008;11(4):499–503.

Bentley KH, Cassiello-Robbins CF, Vittorio L, Sauer-Zavala S, Barlow DH. The association between nonsuicidal self-injury and the emotional disorders: a meta-analytic review. Clin Psychol Rev. 2015;37:72–88.

Nansel TR, Overpeck M, Pilla RS, Ruan WJ, Simons-Morton B, Scheidt P. Bullying behaviors among US youth: prevalence and association with psychosocial adjustment. JAMA. 2001;285(16):2094–100.

Wilton MM, Craig WM, Pepler DJ. Emotional regulation and Display in Classroom victims of bullying: characteristic expressions of Affect, coping styles and relevant Contextual factors. Soc Dev. 2000;9(2):226–45.

Ahmed E, Braithwaite V. What, me Ashamed? Shame management and school bullying. J Res Crime Delinquency. 2004;41(3):269–94.

Smith HM, Betz NE. Development and validation of a scale of Perceived Social Self-Efficacy. J Career Assess. 2000;8(3):283–301.

Toste JR, Heath N. School Response to Non-suicidal Self-Injury. Prev R. 2010;17(1):14–7.

Bresin K, Schoenleber M. Gender differences in the prevalence of nonsuicidal self-injury: a meta-analysis. Clin Psychol Rev. 2015;38:55–64.

Várnai DE, Malinowska-Cieślik M, Madarasová Gecková A, Csémy L, Horváth Z. Do neighbors have more peaceful students? Youth Violence profiles among adolescents in the Czech Republic, Hungary, Poland, and Slovakia. Int J Environ Res Public Health. 2022;19(13):7964.

Download references

Acknowledgements

Not applicable.

Melinda Reinhardt was supported by the National Research, Development, and Innovation Office – NKFIH, Budapest, Hungary under grant number FK 138604. Gyöngyi Kökönyei was supported by the Hungarian National Research, Development, and Innovation Office (FK128614, K143764), the Hungarian Brain Research Program (Grants: 2017 − 1.2.1-NKP-2017-00002), and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022).

Author information

Authors and affiliations.

Doctoral School of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary

Boglárka Drubina

Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary

Boglárka Drubina, Gyöngyi Kökönyei, Dóra Várnai & Melinda Reinhardt

NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary

Gyöngyi Kökönyei

Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary

Heim Pál National Institute of Pediatrics, Budapest, Hungary

Dóra Várnai

14th District Medical Center, Child and Adolescent Psychiatry, Budapest, Hungary

Melinda Reinhardt

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation was performed by MR, data collection and analysis were performed by MR and BD. The first draft of the manuscript was written by BD and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Boglárka Drubina .

Ethics declarations

Competing interests.

The authors (Boglárka Drubina, Gyöngyi Kökönyei, Dóra Várnai, Melinda Reinhardt) declare that they do not have any interests that could constitute a real, potential, or apparent conflict of interest with respect to their involvement in the publication. The authors also declare that they do not have any financial or other relations (e.g., directorship, consultancy, or speaker fee) with companies, trade associations, unions or groups (including civic associations and public interest groups) that may gain or lose financially from the results or conclusions in the study.

Ethics approval and consent to participate

“The study was approved by the Institutional Review Board of ELTE Eötvös Loránd University, Faculty of Education and Psychology. Informed consent was obtained from all the participants and also from the legal guardians of the participants who were below 18 years of age. And all methods were performed in accordance with relevant guidelines and regulation such as Declaration of Helsinki “.

Consent for publication

Additional information, publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1:

Table S1 Standardized regression coefficients and total, direct, and indirect effects related to each model.

Supplementary Material 2:

Table S2 Odds ratios and 95% confidence intervals of associations between independent, mediator and outcome variables.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Drubina, B., Kökönyei, G., Várnai, D. et al. Online and school bullying roles: are bully-victims more vulnerable in nonsuicidal self-injury and in psychological symptoms than bullies and victims?. BMC Psychiatry 23 , 945 (2023). https://doi.org/10.1186/s12888-023-05341-3

Download citation

Received : 18 April 2023

Accepted : 02 November 2023

Published : 14 December 2023

DOI : https://doi.org/10.1186/s12888-023-05341-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• School bullying roles
• Online bullying roles
• Externalizing problems
• Internalizing problems
• Adolescents

BMC Psychiatry

ISSN: 1471-244X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

5 Essays About Bullying

There have always been bullies, but in more recent years, society has become more aware of the impacts of bullying. With the rise of the internet and social media, cyberbullying has also become a serious issue. In 2018, the UNESCO Institute for Statistics reported that ⅓ of young teens worldwide recently experienced bullying. Overall, boys are at a higher risk than girls – 32% compared to 28%. However, in countries with the most incidents of bullying, girls experienced more. Bullying can drive young people to suicide, self-harm, and other tragic consequences. Here are five essays that shed light on the issue:

“The Origins of Bullying”

Author: Hogan Sherrow  | From: Scientific American Sherrow opens his guest post on the Scientific American blog with the story of Jamey Rodemeyer. At age 14, the teen posted messages online describing the pain he endured from bullying and then took his own life. Sadly, this is not uncommon. In this 2011 essay, Sherrow explores why people bully others. Where does this type of behavior come from? To address bullying effectively, we need to understand the roots of bullying. He first defines bullying and presents evidence that bullying is something found in every culture. Sherrow describes it as a “part of the human condition.” Things take a turn into other species as Sherrow asks the question, “Is bullying unique to humans?” Based on research, bullying-like behaviors are found in other animals, including other primates.

This essay presents interesting scientific research on the root of bullying and how it’s evolved in humans. Hogan Sherrow is an assistant professor of anthropology at Ohio University and the director of the Hominid Behavior Research project.

“Instagram Has a Massive Harassment Problem”

Author: Taylor Lorenz | From: The Atlantic This essay opens up with the story of someone who experienced Instagram harassment. At age 14, Brandon joined Instagram to share about his life and rare condition. Soon, he was bombarded with hateful messages, including death threats. It ruined his high school experience. Brandon’s story is just one of countless others where people – often very young teens – are bullied through Instagram. The platform does not have a good track record on monitoring or addressing the bullying. This contrasts sharply with the polished image it projects and markets itself with. While sites like Youtube and Twitter have had bigger dealings with harassment, Instagram seems like an oasis for the internet. What is it doing exactly? According to users who have faced horrific threats, not much. Author Taylor Lorenz is a former staff writer for The Atlantic.

“Shame and Survival”

Author: Monica Lewinsky | From: Vanity Fair Bullying often occurs in a bubble, like a middle school or a social media site, but for Monica Lewinsky, the harassment played out on a much larger scale. In 1998, the 24-year old became the center of a presidential scandal. There were countless jokes made at her expense. Even while Bill Clinton emerged relatively unscathed, the shame followed Lewinsky for years. In this feature from 2014, she recounts her experience with public humiliation, how difficult it was to move on, and the concern she feels for young people today as cyberbullying becomes so prevalent. The essay is a great example of the long-term impact of humiliation on a national scale. Monica Lewinsky is a TV personality, former fashion designer, speaker, and social activist.

“Bullying In the Age of Trump”

By: Emily Bazelon | From: The New York Times Published in November 2016, this op-ed takes a brief look at how bullying evolved with the election of Donald Trump. The Southern Poverty Law Center tracks harassment and hate speech. Since President Trump’s election, they’ve reported a surge in bullying incidents. What this teaches us is that while bullying is always around, it can increase based on what’s going on in the culture. When someone who exhibits classic bullying behavior is put in a position of power, it sends the message that their behavior is acceptable. Emily Bazelon is the author of “Sticks and Stones: Defeating the Culture of Bullying and Rediscovering the Power of Character and Empathy” and a staff writer for the New York Times Magazine.

“Bullying has an impact that lasts years” 

By: Anita Sethi | From: The Guardian Written in recognition of Anti-Bullying Week, this piece describes the author’s personal experience with bullying and its lasting effects. As a child, Sethi experienced physical and emotional bullying. How bullies use language can be the most hurtful. The first thing they often do is take a victim’s name, so dehumanizing them is easier. Years after the bullying, a person’s mental health can suffer lasting consequences. What can be done? Teaching empathy is key. Anita Sethi is a writer, journalist, and contributor to Three Things I’d Tell My Younger Self.

You may also like

15 Political Issues We Must Address

15 Trusted Charities Fighting for LGBTQ+ Rights

16 Inspiring Civil Rights Leaders You Should Know

15 Trusted Charities Fighting for Housing Rights

15 Examples of Gender Inequality in Everyday Life

11 Approaches to Alleviate World Hunger 

15 Facts About Malala Yousafzai

12 Ways Poverty Affects Society

15 Great Charities to Donate to in 2024

15 Quotes Exposing Injustice in Society

14 Trusted Charities Helping Civilians in Palestine

The Great Migration: History, Causes and Facts

About the author, emmaline soken-huberty.

Emmaline Soken-Huberty is a freelance writer based in Portland, Oregon. She started to become interested in human rights while attending college, eventually getting a concentration in human rights and humanitarianism. LGBTQ+ rights, women’s rights, and climate change are of special concern to her. In her spare time, she can be found reading or enjoying Oregon’s natural beauty with her husband and dog.

“Bullying in Schools”: The Aspects of Bullying Research Paper

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

The phenomenon of bullying is one of the most common forms of aggression among children in schools. This problem is of a global nature, and this fact explains the particular attention of scholars to it. In their article, Menesini and Salmivalli (2017) examine the current state of knowledge on the topic and thoroughly discuss all of the aspects of bullying. They include the criteria, which define this behavior, its prevalence, age and gender differences, various types of it as well as a specific precondition for emerging issues regarding the aggressiveness of this population group (Menesini & Salmivalli, 2017). In this way, the study covers all the essential aspects of the matter and its effects on children.

The authors emphasize the inconsistency of previous research on the subject. In the article, they claim that it is related to the prevalence rates due to different measurement methods, but they indicate that the most vulnerable people are youth (Menesini & Salmivalli, 2017). Bullying can be either physical or emotional, and the initiators are more frequently boys (Menesini & Salmivalli, 2017). According to the study, this kind of violence can be considered as such only in the case of repetition, intention, and an imbalance of power, and it is usually caused by prejudice (Menesini & Salmivalli, 2017). On an individual level, this issue emerges due to the lack of social skills and other problems of a bully. As for schools, it is connected to demographic factors and can be resolved only by special programs.

As can be seen from the scope of data covered by the article under consideration, it includes all of the aspects of bullying among children. It allows concluding on its usefulness for developing a presentation on the topic. The principal benefit of this source is the provision of both theoretical and practical information contributing to the inclusion of all the factors, their overall influence, and possible interventions in further research.

Menesini, E., & Salmivalli, C. (2017). Bullying in schools: The state of knowledge and effective interventions . Psychology, Health & Medicine, 22 (sup1), 240-253.

• Masdar City: Project of a Modern Ecological City
• Influence of Violent Video Games on the Aggressiveness of Adolescents
• The Problem of Bullying
• Sociology: Impact of Violence
• Youth Violence in Schools
• Rape: Definition, Different Factors, and How to Prevent
• Understanding Violence in Institutions
• Violence in Institutions: Precipitating Factors
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2022, September 26). “Bullying in Schools”: The Aspects of Bullying. https://ivypanda.com/essays/bullying-in-schools-the-aspects-of-bullying/

"“Bullying in Schools”: The Aspects of Bullying." IvyPanda , 26 Sept. 2022, ivypanda.com/essays/bullying-in-schools-the-aspects-of-bullying/.

IvyPanda . (2022) '“Bullying in Schools”: The Aspects of Bullying'. 26 September.

IvyPanda . 2022. "“Bullying in Schools”: The Aspects of Bullying." September 26, 2022. https://ivypanda.com/essays/bullying-in-schools-the-aspects-of-bullying/.

1. IvyPanda . "“Bullying in Schools”: The Aspects of Bullying." September 26, 2022. https://ivypanda.com/essays/bullying-in-schools-the-aspects-of-bullying/.

Bibliography

IvyPanda . "“Bullying in Schools”: The Aspects of Bullying." September 26, 2022. https://ivypanda.com/essays/bullying-in-schools-the-aspects-of-bullying/.

• Questions Answered
• Publications
• Students with Disabilities
• Spanish - Recursos Acerca de Bullying

Cyberbullying

• Sites for Kids and Teens
• Elementary School Education
• Elementary School Activities
• Middle / High School Education
• Middle / High School Activities
• All In – Year-long Teacher Toolkit
• Show Your Support
• Student Activity Kit
• Share Information
• PACERTalks About Bullying Series
• Carmen's Corner Series
• Music Videos
• Student Created Videos
• What Should You Do?
• Los Angeles Gala
• Take The Pledge
• Cartoon Network, Redraw Your World Without Bullying
• Cartoon Network, Kids Take On Bullying
• Build Community Kit
• Students With Solutions
• The Unity Awards
• Stories of Hope and Resilience
• I Care Because …
• Friends of PACER’s Run Walk Roll
• Definition, Impact and Roles
• Laws and Policy
• Helping Your Child
• Working with the School
• Mobile and Online Safety
• All Products
• Unity Day T-shirt
• Unity Day Posters
• Classroom 5-Poster Series
• Beyond Sticks and Stones
• Instagram Poster Series
• Create A World Without Bullying Resource kit
• Information
• Bullying Prevention 101 Quick Guides
• Cyberbullying Prevention 101 Quick Guides
• Cyberbullying Prevention Infographics
• “What You Can Do”
• Student Action Plan
• Notifying the School
• Cyberbullying: What Parents Should Know
• Spanish Translated Material
• Somali Translated Material
• Students With Disabilities
• Directors Blog

A comprehensive overview of current bullying prevention research conducted by government and higher education agencies.

Rates of Incidence

• One out of every five (20.2%) students report being bullied. ( National Center for Educational Statistics, 2019 )
• A higher percentage of male than of female students report being physically bullied (6% vs. 4%), whereas a higher percentage of female than of male students reported being the subjects of rumors (18% vs. 9%) and being excluded from activities on purpose (7% vs. 4%). ( National Center for Educational Statistics, 2019 )
• 41% of students who reported being bullied at school indicated that they think the bullying would happen again. ( National Center for Educational Statistics, 2019 )
• Of those students who reported being bullied, 13% were made fun of, called names, or insulted; 13% were the subject of rumors; 5% were pushed, shoved, tripped, or spit on; and 5% were excluded from activities on purpose. ( National Center for Educational Statistics, 2019 )
• A slightly higher portion of female than of male students report being bullied at school (24% vs. 17%). ( National Center for Educational Statistics, 2019 )
• Bullied students reported that bullying occurred in the following places: the hallway or stairwell at school (43%), inside the classroom (42%), in the cafeteria (27%), outside on school grounds (22%), online or by text (15%), in the bathroom or locker room (12%), and on the school bus (8%). ( National Center for Educational Statistics, 2019 )
• 46% of bullied students report notifying an adult at school about the incident. ( National Center for Educational Statistics, 2019 )
• The reasons for being bullied reported most often by students include physical appearance, race/ethnicity, gender, disability, religion, sexual orientation. ( National Center for Educational Statistics, 2019 )
• The federal government began collecting data on school bullying in 2005, when the prevalence of bullying was around 28 percent. ( U.S. Department of Education, 2015 )
• One in five (20.9%) tweens (9 to 12 years old) has been cyberbullied, cyberbullied others, or seen cyberbullying. ( Patchin & Hinduja, 2020 )
• 49.8% of tweens (9 to 12 years old) said they experienced bullying at school and 14.5% of tweens shared they experienced bullying online. ( Patchin & Hinduja, 2020 )
• 13% of tweens (9 to 12 years old) reported experiencing bullying at school and online, while only 1% reported being bullied solely online. ( Patchin & Hinduja, 2020 )

Effects of Bullying

• Students who experience bullying are at increased risk for depression, anxiety, sleep difficulties, lower academic achievement, and dropping out of school. ( Centers for Disease Control, 2019 )
• Students who are both targets of bullying and engage in bullying behavior are at greater risk for both mental health and behavior problems than students who only bully or are only bullied. ( Centers for Disease Control, 2019 )
• Bullied students indicate that bullying has a negative effect on how they feel about themselves (27%), their relationships with friends and family (19%), their school work (19%), and physical health (14%). ( National Center for Educational Statistics, 2019 )
• Tweens who were cyberbullied shared that it negatively impacted their feelings about themselves (69.1%), their friendships (31.9%), their physical health (13.1%), and their schoolwork (6.5%). ( Patchin & Hinduja, 2020 ).
• Among students ages 12 – 18 who reported being bullied at school, 15% were bullied online or by text ( National Center for Educational Statistics, 2019 )
• Reports of cyberbullying are highest among middle school students, followed by high school students, and then primary school students ( Centers for Disease Control, 2019 )
• The percentages of individuals who have experienced cyberbullying at some point in their lifetimes have more than doubled (18% to 37%) from 2007-2019 ( Patchin & Hinduia, 2019 )
• When students were asked about the specific types of cyberbullying they had experienced, mean and hurtful comments (25%) and rumors spread online (22%) were the most commonly-cited ( Patchin et al., 2019 )
• The type of cyberbullying tends to differ by gender. Girls were more likely to say someone spread rumors about them online while boys were more likely to say that someone threatened to hurt them online ( Patchin et al., 2019 )

Cyberbullying Among Tweens (9-12 Years Old)

• One in five tweens (20.9%) has been cyberbullied, cyberbullied others, or seen cyberbullying
• 49.8% of tweens said they experienced bullying at school and 14.5% of tweens shared they experienced bullying online
• 13% of tweens reported experiencing bullying at school and online, while only 1% reported being bullied solely online
• Nine out of ten tweens use social media or gaming apps (Patchin & Hinduja, 2020)
• Tweens shared they were engaging on the following sites, apps, or games: YouTube, Minecraft, Roblox, Google Classroom, Fortnite, TikTok, YouTube Kids, Snapchat, Facebook Messenger Kids, Instagram, Discord, Facebook, and Twitch
• Tweens who were cyberbullied shared that it negatively impacted their feelings about themselves (69.1%), their friendships (31.9%), their physical health (13.1%), and their schoolwork (6.5%)
• Tweens reported using a variety of strategies to stop the bullying including blocking the person bullying them (60.2%), telling a parent (50.8%), ignoring the person (42.8%), reporting it to the website or app (29.8%), and taking a break from the device (29.6%)
• Two-thirds of tweens are willing to step in to defend, support, or assist those being bullied at school and online when they see it
• Barriers to helping when tweens witness bullying at school or online included being afraid of making things worse, not knowing what to do or say, not knowing how to report it online, being afraid others kids will make fun of them, being afraid to get hurt, and not knowing who to tell

SOURCE: Patchin, J.W., & Hinduja, S. (2020). Tween Cyberbullying in 2020. Cyberbullying Research Center and Cartoon Network. Retrieved from: https://i.cartoonnetwork.com/stop-bullying/pdfs/CN_Stop_Bullying_Cyber_Bullying_Report_9.30.20.pdf.

Bullying of Students with Disabilities

• Students with specific learning disabilities, autism spectrum disorder, emotional and behavior disorders, other health impairments, and speech or language impairments report greater rates of victimization than their peers without disabilities longitudinally and their victimization remains consistent over time ( Rose & Gage, 2016 )

Bullying of Students of Color

• 23% of African-American students, 23% of Caucasian students, 16% of Hispanic students, and 7% of Asian students report being bullied at school ( National Center for Educational Statistics, 2019 )

Bullying of Students Who Identify or Are Perceived as LGBTQ

Bullying and suicide, interventions.

• Tweens reported using a variety of strategies to stop the bullying including blocking the person bullying them (60.2%), telling a parent (50.8%), ignoring the person (42.8%), reporting it to the website or app (29.8%), and taking a break from the device (29.6%) ( Patchin & Hinduja, 2020 ).
• Two-thirds of tweens are willing to step in to defend, support, or assist those being bullied at school and online when they see it ( Patchin & Hinduja, 2020 ).
• Barriers to helping when tweens witness bullying at school or online included being afraid of making things worse, not knowing what to do or say, not knowing how to report it online, being afraid others kids will make fun of them, being afraid to get hurt, and not knowing who to tell ( Patchin & Hinduja, 2020 ).

References:

Centers for Disease Control, National Center for Injury Prevention and Control (2019). Preventing bullying. Retrieved from https://www.cdc.gov/violenceprevention/pdf/yv/bullying-factsheet508.pdf .

National Center for Educational Statistics. (2019). Student reports of bullying: Results from the 2017 School Crime Supplement to the National Victimization Survey. US Department of Education. Retrieved from http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2015056 .

Patchin, J. W., & Hinduja, S. (2019). 2019 Cyberbullying Data. Cyberbullying Research Center. Retrieved from https://cyberbullying.org/2019-cyberbullying-data .

Patchin, J.W., & Hinduja, S. (2020). Tween Cyberbullying in 2020. Cyberbullying Research Center and Cartoon Network. Retrieved from: https://i.cartoonnetwork.com/stop-bullying/pdfs/CN_Stop_Bullying_Cyber_Bullying_Report_9.30.20.pdf .

Rose, C. A., & Gage, N. A. (2016). Exploring the involvement of bullying among students with disabilities over time. Exceptional Children, 83 , 298-314. Retrieved from http://journals.sagepub.com/doi/abs/10.1177/0014402916667587 .

Updated November 9, 2023

National Office:

8161 Normandale Blvd. | Minneapolis, MN 55437

Los Angeles Area Office:

80 E. Hillcrest Drive, #203 | Thousand Oaks, CA 91360

Founded in 2006, PACER’s National Bullying Prevention Center actively leads social change to prevent childhood bullying, so that all youth are safe and supported in their schools, communities, and online.

[email protected]

• In The News
• History and Impact
• Press Releases

Visit our other sites

• KidsAgainstBullying.org
• TeensAgainstBullying.org

©2023 PACER Center, Inc.

Home — Essay Samples — Social Issues — Cyber Bullying — cyberbullying research among youth

Cyberbullying Research Among Youth

• Categories: Cyber Bullying

About this sample

Words: 518 |

Published: Jun 13, 2024

Words: 518 | Page: 1 | 3 min read

Cite this Essay

Let us write you an essay from scratch

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

Get high-quality help

Prof. Kifaru

Verified writer

• Expert in: Social Issues

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

1 pages / 586 words

4 pages / 1719 words

4 pages / 1757 words

7 pages / 3008 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Cyber Bullying

Cyberbullying has become an alarming issue in today's digital age. With the widespread use of technology and social media platforms, individuals are increasingly vulnerable to online harassment and abuse. This essay explores the [...]

The issue of police brutality and racism has been a longstanding and contentious one in the United States. The tragic deaths of numerous black individuals at the hands of law enforcement officers have sparked protests and calls [...]

Social media platforms have become an integral part of our daily lives, allowing us to connect, share, and interact with others in ways that were previously unimaginable. However, along with the many benefits of social media, [...]

Cyber bullying has become a prevalent issue in today's society, with the rise of social media and digital communication. This form of bullying involves the use of electronic devices to harass, intimidate, or harm others. In this [...]

So, what is cyberbullying? Cyberbullying is essentially a form of bullying that occurs online, often through social networking sites, and involves posting negative words with the intention of humiliating others. Sadly, this form [...]

Cyberbullying is a complex issue that manifests differently across various cultural contexts. This essay has explored the manifestations of cyberbullying in Western and non-Western cultures, identified global challenges in [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

Masks Strongly Recommended but Not Required in Maryland, Starting Immediately

Due to the downward trend in respiratory viruses in Maryland, masking is no longer required but remains strongly recommended in Johns Hopkins Medicine clinical locations in Maryland. Read more .

• Vaccines  
• Masking Guidelines
• Visitor Guidelines  

7 Tips for Helping Your Child Deal with Bullying

Featured Expert:

Melissa Faith, PhD

Bullying is a serious issue that affects up to 75 percent of kids at some point between kindergarten and 12th grade. Most of those kids will be OK in the end, but 10–20 percent of kids will be chronically bullied and at risk for poor physical and mental health outcomes, such as physical ailments, depression and anxiety, aggression and, in extreme cases, suicide, as well as lower graduation rates and lower career placement.

Chronic bullying has three components: a power differential between the child and the aggressor, repeated bullying day after day, and a social sanctioning aspect where the social structure maintains the victim’s status.

This can be very troubling for parents and caregivers, but there are things you can do to help a child who is a victim or committer of bullying.  Melissa Faith, Ph.D., ABPP , a clinical pediatric psychologist and board certified specialist in clinical child and adolescent psychology at Johns Hopkins All Children’s Hospital , offers these tips for families:

Encourage your child to be a friend.

• As a parent, you will know where your child fits into their school’s social structure. For children who have high social status–have a lot of friends, teachers like them, they’re popular and make friends easily–encourage them to stand up for children who are bullied. Let them know that it is OK to say to the aggressor, “That’s not OK," and then take the victim under their wing. If they aren’t comfortable with that, they can always tell a teacher or another trusted adult.
• As children move into their adolescence or older teenage years, very confident children may want to do more. Encourage them to reach out to their friends and to reach out to other children at school.

Create a culture of inclusion.

• Engage in emotion coaching to help build emotional skills. Help your child understand his or her feelings and why other people may feel differently.
• Model pro-social behaviors for your children. Talk through what you’ve done and why you’ve acted that way.
• As a parent, you can reach out to the parents of a friend your child trusts and encourage them to help and have conversations with their child about what it means to leave someone out and what things they can do when they see someone being excluded from activities.

Support extracurricular activity.

• Mastering at least one specialty area can help protect against the long-term negative impacts of chronic bullying. Cooperative, team-based activities also can help foster social skills and provide opportunities for your child to build friendships with other children who have similar interests. Activities can be based in the arts, such as choir, theater or dance, or athletics, such as track or football.

Foster “best friendships.”

• Having at least one best friendship can protect children against the negative effects of chronic bullying. As a parent, you can foster best friendships. Encourage your child to invite a close friend to do things together and help them spend more time with the children important to them.

Get involved in your child’s school.

• In Florida, every school district is required to have a written policy about what they’ll do if there is bullying, what the consequences will be, and how they will offer help.
• If there is a bullying incident, make a request, in writing, to your child’s school counselor and/or principal, respectfully asking for an investigation. After that, schedule a meeting with everyone who might be important in the incident (e.g., the school counselor, your child’s teacher) and ask questions, make specific requests of the school and leave the meeting with a written plan saying exactly what is going to be done. Remember that your child’s teacher could have great strategies that have worked in the classroom in the past, so it can be helpful to pick the teacher’s brain about his or her suggested strategies. Schedule a follow up meeting about a month later.
• Many victims of chronic bullying find that their victim status is very hard to change, in part because other children in the classroom may be afraid to stand up to the bully and/or because other children in the classroom have started to adopt a view that the child is a victim. For these reasons, sometimes the best thing to do is to move the victim into another classroom. Note that moving the bully into another classroom is rarely helpful because chronic bullying is really about the entire social structure in the classroom—not just about the bully and victim.

Find security against cyberbullying.

• Cyberbullying is different from other forms of bullying because it takes place online where the bullies can remain anonymous and parents may not even be aware of what is going on. Kids who are being cyberbullied might not tell parents because they may be afraid of losing access to technology or of consequences if they were on sites they should not have been on.
• Young kids can be instructed that only certain icons on the phone or tablet home screen are OK for them to touch.
• Have discussions with older kids about what your expectations are. Set up social media accounts with them. Know their passwords. Inform your child that you will occasionally login to their social media accounts and, if the child has changed his or her password to prevent parents’ access, the child’s access to social media and/or electronics will be suspended. If necessary, there are tools available that can track what sites your child visits and what they say online.

Don’t be afraid to seek help.

• If your child is a victim of bullying, or if your child is a bully, and the situation has surpassed what you as a parent have the resources to fix, help is available. Talking to a mental health professional can give your family the tools and support to work through difficult situations. 

Find a Treatment Center

• Johns Hopkins Children's Center
• Johns Hopkins All Children's Hospital
• Child and Adolescent Psychiatry

Find Additional Treatment Centers at:

• Howard County Medical Center
• Sibley Memorial Hospital
• Suburban Hospital

Pediatric Care at Johns Hopkins Medicine

Center for behavioral health at johns hopkins all children's hospital.

The Center for Behavioral Health in the Institute for Brain Protection Sciences at Johns Hopkins All Children’s Hospital in St. Petersburg, Florida, provides evidence-based mental health screening, consultation, evaluation and treatment.

Child and Adolescent Psychiatry at Johns Hopkins Children's Center

The Division of Child and Adolescent Psychiatry at Johns Hopkins Children's Center in Baltimore, Maryland offers hospital-based intensive evaluation and treatment programs, as well as outpatient services

Request an Appointment

Student Athlete Mental Health

Does going back to school mean going back to bullying?

Supporting Children's Mental Health After Violence

Related Topics

• Kids' and Teens' Health

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Campbell Syst Rev
• v.17(2); 2021 Jun

Effectiveness of school‐based programs to reduce bullying perpetration and victimization: An updated systematic review and meta‐analysis

Hannah gaffney.

1 Institute of Criminology, University of Cambridge, Cambridge UK

Maria M. Ttofi

David p. farrington, executive summary/abstract.

Bullying first emerged as an important topic of research in the 1980s in Norway (Olweus), and a recent meta‐analysis shows that these forms of aggression remain prevalent among young people globally (Modecki et al.). Prominent researchers in the field have defined bullying as any aggressive behavior that incorporates three key elements, namely: (1) an intention to harm, (2) repetitive in nature, and (3) a clear power imbalance between perpetrator and victim (Centers for Disease Control and Prevention; Farrington). There are many negative outcomes associated with bullying perpetration, such as: suicidal ideation (Holt et al.), weapon carrying (Valdebenito et al.), drug use (Ttofi et al.), and violence and offending in later life (Ttofi et al.). Bullying victimization too is associated with negative outcomes such as: suicidal ideation (Holt et al.), anxiety, low self‐esteem and loneliness (Hawker& Boulton). Therefore, school bullying is an important target for effective intervention, and should be considered a matter of public health concern.

The objective of this review is to establish whether or not existing school‐based antibullying programs are effective in reducing school‐bullyng behaviors. This report also updates a previous meta‐analysis conducted by Farrington and Ttofi. This earlier review found that antibullying programs are effective in reducing bullying perpetration and victimization and a primary objective of the current report is to update the earlier analysis of 53 evaluations by conducting new searches for evaluations conducted and published since 2009.

Search Methods

Systematic searches were conducted using Boolean combinations of the following keywords: bully*; victim*; bully‐victim; school; intervention; prevention; program*; evaluation; effect*; and anti‐bullying . Searches were conducted on several online databases including, Web of Science, PscyhINFO, EMBASE, EMBASE, DARE, ERIC, Google Scholar, and Scopus. Databases of unpublished reports, such as masters' and doctoral theses (e.g., Proquest) were also searched.

Selection Criteria

Results from systematic searches were screened thoroughly against the following inclusion criteria. To be included in this review, a study must have: (1) described an evaluation of a school‐based antibullying program implemented with school‐age participants; (2) utilized an operational definition of school‐bullying that coincides with existing definitions; (3) measured school‐bullying perpetration and/or victimization using quantitative measures, such as, self‐, peer‐, or teacher‐report questionnaires; and (4) used an experimental or quasi‐experimental design, with one group receiving the intervention and another not receiving the intervention.

Data Collection and Analysis

Of the 19,877 search results, 474 were retained for further screening. The majority of these were excluded, and after multiple waves of screening, 100 evaluations were included in our meta‐analysis. A total of 103 independent effect sizes were estimated and each effect size was corrected for the impact of including clusters in evaluation designs. Included evaluations were conducted using both randomized ( n  = 45; i.e., randomized controlled trials/RCTs) and nonrandomized ( n  = 44; i.e., quasi‐experimental designs with before/after measures; BA/EC) methodologies. All of these studies included measures of bullying outcomes before and after implementation of an intervention. The remaining 14 effect sizes were estimated from evaluations that used age cohort designs. Two models of meta‐analysis are used to report results in our report. All mean effects computed are presented using both the multivariance adjustment model (MVA) and random effects model (RE). The MVA model assigns weights to primary studies in direct proportion to study level sampling error as with the fixed effects model but adjusts the meta‐analytic standard error and confidence intervals for study heterogeneity. The RE model incorporates between‐study heterogeneity into the formula for assigning weights to primary studies. The differences and strengths/limitations of both approaches are discussed in the context of the present data.

Our meta‐analysis identified that bullying programs significantly reduce bullying perpetration (RE: odds ratio [OR] = 1.309; 95% confidence interval [CI]: 1.24–1.38; z  = 9.88; p  < .001) and bullying victimization (RE: OR = 1.244; 95% CI: 1.19–1.31; z  = 8.92; p  < .001), under a random effects model of meta‐analysis. Mean effects were similar across both models of meta‐analysis for bullying perpetration (i.e., MVA: OR = 1,324; 95% CI: 1.27–1.38; z  = 13.4; p  < .001) and bullying victimization (i.e., MVA: OR = 1.248; 95% CI: 1.21–1.29; z  = 12.06; p  < .001). Under both computational models, primary studies were more effective in reducing bullying perpetration than victimization overall. Effect sizes varied across studies, with significant heterogeneity between studies for both bullying perpetration ( Q  = 323.392; df  = 85; p  < .001; I 2  = 73.716) and bullying victimization ( Q  = 387.255; df  = 87; p  < .001; I 2  = 77.534) outcomes. Analyses suggest that publication bias is unlikely. Between‐study heterogeneity was expected, given the large number of studies included, and thus, the number of different programs, methods, measures and samples used.

Authors' Conclusions

We conclude that overall, school‐based antibullying programs are effective in reducing bullying perpetration and bullying victimization, although effect sizes are modest. The impact of evaluation methodology on effect size appears to be weak and does not adequately explain the significant heterogeneity between primary studies. Moreover, the issue of the under‐/over‐estimation of the true treatment effect by different experimental designs and use of self‐reported measures is reviewed. The potential explanations for this are discussed, along with recommendations for future primary evaluations. Avenues for future research are discussed, including the need further explain differences across programs by correlating individual effect sizes with varying program components and varying methodological elements available across these 100 evaluations. Initial findings in the variability of effect sizes across different methodological moderators provide some understanding on the issue of heterogeneity, but future analyses based on further moderator variables are needed.

1. PLAIN LANGUAGE SUMMARY

1.1. interventions to reduce school bullying perpetration and victimization are effective.

Bullying is a ubiquitous form of aggression in schools worldwide. Intervention and prevention programs targeting school bullying perpetration and victimization are effective, yet more research is needed to understand variability in effectiveness.

The main findings of our review are that bullying programs were effective in reducing bullying perpetration outcomes by roughly 18–19% and bullying victimization by roughly 15–16%. There are substantial variations in effects, and the reasons for these variations require further research.

1.2. What is this review about?

Bullying is defined as aggressive behaviors that occur repeatedly over time between two or more individuals. Typically, there is a clear power imbalance between victims and bullies, either socially or physically. Furthermore, bullying behaviors are those that are committed intentionally to harm the victim.

What is the aim of this review?

The aim of this review is to summarise findings from studies of the effectiveness of school‐based antibullying programs in reducing both bullying perpetration and victimization will be reported. The review summarizes 100 studies, with the largest number being from the United States.

1.3. What studies are included?

To be included in this review, primary studies must have evaluated a specific intervention program that targeted bullying perpetration and/or victimization outcomes in school‐aged children, that is, typically between four and 18 years old. Studies must have used two experimental groups of children, one that received the intervention, and one that did not, and applied quantitative measures of bullying behavior (perpetration and/or victimization) that coincided with our operational definition of bullying.

Our final meta‐analytic review includes 100 studies of the effectiveness of antibullying programs. The largest number of studies came from the United States, with most other studies from Canada and Europe.

1.4. What are the findings of this review?

Antibullying programs are effective in reducing bullying perpetration outcomes by roughly 18–19% and bullying victimization by roughly 15–16%.

Variability in the effectiveness of antibullying programs was associated with differences in methodological designs, types of programs and geographical regions. Interventions evaluated using age cohort designs collectively gave the largest overall effect for both bullying perpetration and bullying victimization.

Limitations of the results are similar to those of previous reviews; for example, the reliance of self‐reported measurements of bullying may suggest the change is in reports of bullying perpetration/victimization and not behavioral change.

1.5. What do the findings of this review mean?

The findings indicate that school‐based bullying intervention and prevention programs can be effective in reducing both bullying perpetration and victimization, although the effect is, overall, modest.

The effectiveness of antibullying programs is an important finding with implications for public health and educational policy. However, our review did identify that there are variations in the effectiveness of intervention programs. Future research is needed to explore the reasons for these variations.

1.6. How up‐to‐date is this review?

This report forms an update of an earlier review (Farrington & Ttofi,  2009 ). The review authors searched for studies published up to December 2016.

2. BACKGROUND

Bullying first emerged as an important topic of research in the 1980s, following the tragic suicides of young boys in Norway, the reason for which was attributed to bullying victimization (Olweus,  1993 ). Today, this form of aggressive behavior remains a prevalent problem among young people globally. For example, a recent meta‐analysis of 80 international studies discovered prevalence levels of 34.5% and 36% for bullying perpetration and bullying victimization respectively (Modecki et al.,  2014 ).

Notably, bullying is a matter of public health, impacting the life outcomes of both bullies and victims, in varying ways (Arseneault et al.,  2010 ; Masiello & Schroeder,  2014 ; Ttofi et al.,  2012 ). Given its long‐term effects, it is imperative that effective intervention efforts are put in place in order to alleviate this troubling school phenomenon (Ttofi,  2015 ).

2.1. Defining school bullying

In order to adequately determine which interventions will effectively reduce bullying behaviors, it is important that researchers and educators start by accurately assessing the prevalence of involvement in school bullying (Swearer et al.,  2010 ). There remains some degree of disagreement in relation to definitive cut‐off points for involvement in bullying (Solberg & Olweus,  2003 ; Swearer et al.,  2010 ) and methods utilized for the assessment of bullying (Smith et al.,  2002 ; Swearer et al.,  2010 ). However, there is better agreement in regard to the defining criteria for school bullying.

Prominent researchers in the field have defined bullying as any aggressive behavior that incorporates three core elements, namely: (1) an intention to harm, (2) repetitive in nature, and (3) a clear power imbalance between perpetration and victim (Centers for Disease Control and Prevention,  2014 ; Farrington,  1993 ; Olweus,  1993 ). In other words, bullies are individuals who intend to cause harm to their victims through their actions, over a long period of time. Furthermore, victims of bullying are typically less powerful than bullies, or groups of bullies, and feel that they cannot easily defend themselves. This may be due to a physical or social power imbalance.

There are many forms of bullying, for example, school‐bullying, workplace bullying, sibling bullying and, most recently, cyberbullying. The present review is concerned only with face‐to‐face school‐bullying, namely, bullying that occurs in schools between individuals, usually aged between 4 and 18 years old. In the school context, bullying is a complex social phenomenon, that often does not happen between the bully and victim in isolation (Salmivalli,  2010 ). For example, individuals can be involved in bullying, not only as bullies, victims, or bully‐victims, but also as bystanders, defenders, or reinforcers (Zych et al.,  2017 ).

Cyberbullying is another form of aggressive behaviors that may occur within a school community, and previous research has found a significant overlap between offline (i.e., school‐bullying or face‐to‐face bullying) and online bullying (Baldry et al.,  2017 ). There is currently very little information about the effectiveness of intervention programs designed to reduce cyberbullying or whether school‐based programs that also target face‐to‐face bullying can impact online bullying concurrently.

2.2. The importance of addressing school bullying

School‐bullying is a strong risk marker for several negative behavioral, health, social, and/or emotional problems. A recent comprehensive review of systematic reviews highlighted that the impact of school‐bullying can occur concurrently with perpetration and/or victimization, but also later in life (Zych et al.,  2015 ). Previous studies have found that bullying victimization is often followed by negative mental health outcomes such as: increased suicidal ideation (e.g., Holt et al.,  2015 ); generalized or social anxiety, low self‐esteem and loneliness (e.g., Hawker & Boulton,  2000 ); psychotic symptoms (e.g., van Dam et al.,  2012 ); depression (e.g., Ttofi et al.,  2011a ,  2011b ); sleeping problems (Geel et al.,  2016 ); and other psychosomatic symptoms (Gini & Pozzoli,  2013 ).

Bullying perpetration, on the other hand, has been linked to several negative outcomes such as: suicidal ideation and suicidal attempts (Holt et al.,  2015 ); weapon carrying (Valdebenito et al.,  2018 ); drug use (Ttofi et al.,  2016 ); and violence and offending in later life (Ttofi et al.,  2011b ,  2012 ). Although involvement in school bullying is not necessarily a causal factor for undesirable life outcomes, research has found that there is an apparent association. It may be the case that the experience of school bullying functions as a stepping stone toward undesirable life outcomes (Arseneault et al.,  2010 ).

Moreover, involvement in school bullying, as either a bully or a victim, has been found to correlate with factors such as low academic achievement (Strøm et al.,  2013 ), truancy from school (Gastic,  2008 ), and drug use (Valdebenito et al.,  2015 ). Such factors are common risk factors for youth offending and delinquency (Farrington & Welsh,  2008 ). Therefore, a bullying prevention program could serve as a crime prevention program, as well as a form of promoting public health.

3. OBJECTIVES

It is clear that school bullying is an important target for effective intervention and prevention. Bullying is an ethical problem as well as a developmental one: targeting school bullying facilitates the process of optimal psychological development but it also addresses the question of human rights, especially the rights of the child (Sercombe & Donnelly,  2013 ). The aim of this paper is to provide an up‐to‐date systematic and meta‐analytical exploration of the effectiveness of school‐based antibullying programs. As such, the present report updates an earlier systematic and meta‐analytic review (Farrington & Ttofi, 2009 ; Ttofi & Farrington,  2011 ), by including evidence from an earlier report, and all available evaluations of antibullying programs since 2009.

It is hoped that this new evidence base will assist policy‐makers and practitioners working in the field of bullying prevention. Farrington and Ttofi's ( 2009 ) review concluded that school‐based antibullying programs are effective in reducing both bullying perpetration (OR = 1.36; 95% CI: 1.26–1.47; z  = 7.86; p  < .0001) and bullying victimization (OR = 1.29; 95% CI: 1.18–1.42; z  = 5.61; p  < .0001). Their review had a major impact on the field of bullying intervention and prevention, and in the 9 years that have passed since its publication there has been a wealth of new research.

Therefore, the aim of the present report is to conduct systematic searches for new evaluations of antibullying programs, and also update earlier analysis by including their 53 evaluations.

The initial stage of any meta‐analysis involves conducting a thorough and systematic search of all the existing and relevant literature (Lipsey & Wilson,  2001 ; Littell et al.,  2008 ). Using predetermined keywords and strict inclusion/exclusion criteria, a systematic review aims to identify, screen, appraise, and synthesize all relevant empirical studies (Zych et al.,  2017 ). In this way, systematic bias is avoided.

4.1. Inclusion and exclusion criteria

To be included in the present systematic review, a set of strict inclusion and exclusion criteria were employed to guide searches. These criteria were identical to those used in the previous meta‐analysis (Farrington & Ttofi,  2009 ). Specifically, to be included, primary studies must:

• (1) Describe an evaluation of a school‐based antibullying program implemented with school‐age participants (depending on the site of evaluation, ages may vary between 4 and 18 years of age);
• (2) Utilize an operational definition of school‐bullying that coincides with existing definitions (e.g., CDC,  2014 ; Farrington,  1993 ; Olweus,  1993 );
• (3) Measure school‐bullying perpetration and/or victimization using quantitative measures, such as, self‐, peer‐, or teacher‐report questionnaires; and
• (4) Use an experimental or quasi‐experimental design, with one group receiving the intervention and another (control group) not receiving the intervention. Nonrandomized studies had to measure outcomes before and after the intervention.

As a result, the present systematic review excludes studies that evaluate the effectiveness of intervention programs targeting alternative forms of bullying, such as cyber‐bullying (e.g., Del Rey et al.,  2015 ), general aggression (e.g., Leff et al.,  2010 ), and school violence (e.g., Giesbrecht et al.,  2011 ). Other studies were excluded because they measured bullying‐related nonbehavioral outcomes, for example, “attitudes towards bullying” (e.g., Earhart,  2011 ), or coping strategies for dealing with victimization (e.g., Watson et al.,  2010 ).

In addition, studies conducted with special needs, delinquent, or psychiatric populations were excluded (e.g., Espelage et al.,  2015 ), so that results could be generalizable to the wider mainstream school population. Studies using qualitative measures of effectiveness, such as participant perceptions of the effectiveness of the program (e.g., Fletcher et al.,  2015 ), were also excluded.

4.2. Searches 1

In order to identify potentially includable studies, Boolean searches were conducted using multiple combinations of the following keywords: bully*; victim*; bully‐victim; school; intervention; prevention; program*; evaluation; effect*; and anti‐bullying . A full description of the syntax used is provided in Appendix A.

Searches were conducted on several online databases, including, but not limited to: Web of Science, 2 PsychINFO, EMBASE, DARE, ERIC, and Scopus. Google scholar ( www.scholar.google.co.uk ) was also searched. A full list of databases searched is provided in Table  1 . EBSCOhost was used as a platform to search multiple databases concurrently and such databases are indicated in Table  1 .

Online platforms and databases manually searched

Note: EBSCOhost was used as a platform to search multiple databases concurrently. Such databases are marked with an *.

Databases of unpublished reports (e.g., ProQuest Dissertations and Theses Solutions) were also searched to include gray literature in our review. This should help to minimize potential publication bias linked to larger or significant effect sizes (Easterbrook et al.,  1991 ; McAuley et al.,  2000 ). In addition, evaluation studies included by previous systematic reviews were scanned, based on the name of each program, for additional‐updated evaluation results (i.e., Cantone et al.,  2015 ; Chalamandaris & Piette,  2015 ; Evans et al.,  2014 ; Jiménez‐Barbero et al.,  2012 ,  2016 ).

Studies included in the previous review (Farrington & Ttofi,  2009 ; Ttofi & Farrington,  2011 ), were also included in the present systematic review. Searches for the present review were conducted up to the end of December 2016, 3 for empirical studies published during and since 2009.

4.3. Screening

Our searches of the literature produced approximately 19,877 reports that were screened for eligibility. Based on the title and abstract, a total of 474 primary studies were identified as relevant, were obtained and subjected to further screening. Studies were allocated to six categories based on their relevance to the current meta‐analysis. A description of each category is provided in Table  2 . Screening was undertaken by the first author (H. G.), under the supervision of the second author (M. T.), in a collaborative format. H. G. reviewed eligible studies, and any queries were settled in discussion with M. T.

Relevance scale categories used in screening

The initial wave of screening excluded 258 of these primary studies. At this stage, studies were excluded because they: (1) did not evaluate a specific antibullying program (Category 1; n  = 107); (2) reviewed several different antibullying programs (Category 2; n  = 108); or (3) did not report empirical quantitative data from an evaluation of a specific antibullying program (Category 3; n  = 43).

A second wave of screening excluded a further 133 studies (Category 4; see Table  3 ). Primary studies were excluded at this stage because they: (1) reported irrelevant outcomes; (2) did not have an adequate control group; or (3) did not meet specified methodological criteria. The screening process is described in detail in Figure  1 . In total, 83 studies published since 2009 were included in our updated systematic review (Category 5).

Descriptions of category four studies

Screening of studies

In addition, five studies were identified during searches conducted for a meta‐analytical review of cyberbullying prevention programs (Gaffney et al.,  2018 ). These studies were missed during systematic searches for the current review (i.e., Kaljee et al.,  2017 ; Ortega‐Ruiz et al.,  2012 ; Ostrov et al.,  2015 ; Silva et al.,  2016 ; Solomontos‐Kountouri et al.,  2016 ). One of these studies (i.e., Kaljee et al.,  2017 ) has a publication date outside of the range of our searches. However, it was included because it was available online in 2016.

To provide the most up‐to‐date analysis of school‐based bullying prevention and intervention programs, therefore, a total of 88 newly identified studies are included in the present systematic review.

5. DATA EXTRACTION

After identifying studies eligible for inclusion in the present systematic and meta‐analytical review detailed information about the antibullying programs, sample involved, and evaluation design were extracted from primary studies. The following chapter outlines the coding framework applied in greater detail.

Table  4 also outlines each piece of information extracted. Information was extracted from primary studies under four main headings: (1) Descriptives, (2) Design, (3) Program, and (4) Outcomes. Additionally, the following section outlines information extracted from primary studies in order to create a risk of bias index. Table  5 outlines the items utilized to assess risk of bias for each of the methodological designs included in the present report. Details of the risk of bias results for each study is provided in Appendix B.

Coding framework

Abbreviations: BA/EC, quasi‐experiments with before and after measures of bullying (nonrandomized); exp, experimental group; OBPP, Olweus Bullying Prevention Program; RCT, randomized controlled trial.

Risk of bias tool

Abbreviations: AC, age cohort design; BA/EC, quasi‐experimental design with before and after measures of bullying; RCT, randomized controlled trial.

This procedure was carried out by the first author in consultation with the second and third authors. 4 There were a number of studies from the previous Campbell Collaboration report (i.e., Farrington & Ttofi,  2009 ) for which full texts were unavailable and thus, were excluded from several of the moderator analyses.

5.1. Descriptive

Various pieces of descriptive information were extracted from each of the 100 evaluations included in the present report. Information specific to the evaluation, such as the location or the start/end date, were recorded along with detailed information concerning the sample.

The total sample size and also the n of the relevant experimental and control groups were recorded. Age was extracted in two ways. First, where studies reported the mean age, or the age range (i.e., 8–10 years old) of participants this was recorded. Second, some studies did not report the age in years of participants, but we were able to record the school grade of included samples (i.e., Grades 4–6). Where reported, the % of females and males included in the sample was extracted.

We also coded descriptive information about the publication of the evaluation. Specifically, the type of publication and the publication year was recorded. The former represents a categorical moderator reflected whether or not the evaluation was published via the following channels, in order of hypothesized negative correlation with bias: (1) peer‐reviewed journal article; (2) chapter in an edited book/book; (3) governmental report or similar; (4) correspondence; and (5) unpublished masters or doctoral theses.

Correspondence was included to reflect data obtained from multiple evaluations of the Olweus Bullying Prevention Program (OBPP) sent to the second (M. M. T.) and third (D. P. F.) authors in preparation of their earlier Campbell review. Where evaluation data had been published in multiple formats, we favored the category associated with the least potential bias. For example, Domino ( 2011 ) reported the results of an evaluation of Take the LEAD program in a doctoral dissertation, but later published these results in a peer‐reviewed journal (i.e., Domino,  2013 ). In this scenario, the included study was coded as “article.”

5.2. Design

Included studies were further categorized according to several aspects of the research design used. We coded information regarding both the measures (i.e., instruments to measure bullying behaviors) and research design.

In relation to measurements of bullying, we recorded the timeframe (i.e., past 3 months or “ever”) in which participants were asked to report on experiences of bullying, the type of report used (i.e., self‐, peer‐, or teacher‐report), and data collection points (i.e., baseline, postintervention, 3‐month follow‐up, etc.). We also noted whether the measure was a continuous scale or a global item and whether bullying perpetration, victimization, or both, outcomes were measured.

As for the research design, we recorded information regarding the unit of allocation (or unit of randomization for RCTs; see below), the number of “clusters” included, whether groups were matched at baseline, and the number of experimental or control groups. For example, Elledge et al. ( 2010 ) included multiple control groups: matched controls and nonmatched controls.

Information about the evaluation methodology was also extracted from primary reports. The types of evaluation methodologies included in the present report are now described in further detail.

5.2.1. Evaluation methodology

In order to optimize the comparability of effect sizes, primary studies included in a meta‐analysis should use the same, or at least conceptually similar, research designs (Wilson,  2010 ). Following Farrington and Ttofi's ( 2009 ) criteria, we searched for evaluations using any of the following four research designs:

• (1) Randomized controlled trials (RCTs);
• (2) Before‐after/quasi‐experimental‐control designs (BA/EC);
• (3) Other quasi‐experimental designs; and
• (4) Age cohort designs.

Each of these methodologies varied on four key elements: as randomization of participants (or clusters of participants); use of experimental and control groups; and administration of quantitative bullying measures before and after intervention.

For example, all studies coded as RCT had to include random assignment to experimental conditions (i.e., intervention and control groups) but did not have to use before and after measures of bullying outcomes. RCTs are considered to be the “gold standard” of experimental evaluations (Weisburd et al.,  2001 ). Random assignment of a large number of units is used as a way in which evaluators can also randomize possible confounding variables between groups. As a result, we can infer that any observed differences result from the experimental manipulation (Farrington,  1983 ). The assumption is that randomization ensures that both observed and unobserved variables that may impact the results of an evaluation are also randomly distributed between groups. However, problems may arise if the unit‐of‐allocation, the unit‐of‐randomization, and the unit‐of‐analysis do not align.

Before‐after/quasi‐experimental‐control (BA/EC) designs, are conceptually similar to RCTs, but they do not involve random assignment to experimental conditions. Instead, participants or clusters of participants may be assigned to the intervention or control group on a self‐selected basis (e.g., Menesini et al.,  2012 ), for convenience (e.g., Sapouna et al.,  2010 ), or based on a greater need for intervention (e.g., Losey,  2009 ). Thus, BA/EC designs may be subject to selection biases (Farrington & Petrosino,  2001 ) that may reduce the validity of the results. These can be controlled if outcomes are measured before and after the intervention. Studies coded as BA/EC in the present report all used experimental and control groups but did not randomly assign participants to conditions. They also had to measure bullying outcomes before and after implementation of the intervention.

In contrast, studies categorized in the current review as using “other quasi‐experimental” designs utilized experimental and control conditions, without random assignment, but did not measure bullying behaviors before the intervention. Bullying outcomes were only measured after the implementation of an intervention in these studies. Therefore, selection bias is may be a threat to the internal validity of the results in such designs, which could have possibly attributed to pre‐existing differences between the groups (Farrington, 2003 ). For this reason, a decision was made to omit these designs from this updated meta‐analysis. Thus, relevant evaluations identified in the earlier Campbell Review and any new evaluations (since 2009) using this methodological design were excluded from the new meta‐analyses (see later).

In an age cohort design, students of a particular age X are initially assessed in the 1st year and serve as the control group for the evaluation of an intervention. Then, all students receive the intervention, and different students of the same age X (in the same school, in the 2nd year) serve as the experimental group (see Kärnä et al.,  2013 ). This design, which is largely used in evaluations of the OBPP, deals with some selection effects, since it ensures that experimental and control children are matched on age and school, and it deals with some threats to internal validity (e.g., ageing and maturation). However, this design may be influenced by period and testing effects, and the experimental and control groups may differ on other uncontrolled variables.

Studies employing RCTs, BA/EC, and age cohort designs were included in the present systematic and meta‐analytic review. Because of the potential threat to internal validity, we excluded studies ( n  = 9) in the other quasi‐experimental design category because they are poorly controlled and vulnerable to selection effects. Additionally, the four studies included in the earlier review that used an “other quasi‐experimental” design were excluded from the present systematic review.

5.3. Program

Using a socio‐ecological systems theory framework (Bronfenbrenner, 1979 ) and the previous meta‐analysis (i.e., Farrington & Ttofi,  2009 ) as guidelines, information about the specific intervention program was recorded. General details about the intervention, such as the name of the program (where relevant) and the aim of the intervention (e.g., Silva et al.,  2016 ) were noted along with more detailed information about the antibullying programs.

Intervention components at multiple levels of the socio‐ecological model (i.e., individual, peer, parent, and teacher, etc.) were recorded, such as work with peers, parental involvement, teacher training and whole‐school‐approach. Therefore, a brief description of each antibullying program based on this information is provided in Table  6 .

Systematic review results

In addition to specific program elements included in interventions, we also coded for possible sources of bias in evaluations and intervention development. Conflict of interest (COI) has previously been reported to impact evaluation results of many interventions and is a growing area of interest (COI; Eisner & Humphreys,  2012 ) with studies identified as having higher COI associated with larger overall effect sizes. Eisner and Humphreys outline many other possible sources of COI, such as financial gain to the evaluator, but this information was difficult to obtain for antibullying programs. Thus, a simple indication of potential COI was utilized.

We primarily focused on the overlap between individuals included as author/coauthor on the evaluation study, is also included on previous evaluations of the same program (e.g., NoTrap!; Menesini et al.,  2012 ; Palladino et al.,  2012 ,  2016 ), or is in fact referenced as the developer of that particular program (e.g., Tsiantis et al.,  2013 ). If no reference to a publication relating to the specific program was included, we concluded that the author had developed the program, and thus, the evaluation was deemed high risk.

Program specificity refers to whether the intervention program was specifically targeting bullying outcomes, or if many other outcomes were also included. Targeted programs are suggested to be more effective than generalized programs that aim to reduce many different behaviors in one intervention. Highly specific programs (i.e., those that only included bullying outcomes and very few others) were coded as “high.” Thus, programs that were less specific and included many other outcomes in addition to bullying measures were considered “low.” A third category was created (i.e., “medium”) to include studies that did multiple other outcomes in addition to bullying outcomes, but these additional variables were bullying‐related.

5.4. Outcomes

We also extracted several pieces of statistical information from primary studies that was required for the estimation of effect sizes. Statistics for bullying behaviors, for example, means and standard deviations or sample sizes and percentage of bullies and/or victims, were extracted for experimental and control groups at baseline and immediately postintervention timepoints.

We also coded bullying data for additional follow‐up timepoints where this information was reported by primary studies. Data was extracted and recorded separately for independent samples (i.e., female and male, Palladino et al.,  2016 ; older and younger, Baldry & Farrington, 2001) and different measures. For example, data for both self‐ and peer‐report measures were extracted from Beery and Hunt (2009) and for different forms of bullying (e.g., Frey et al.,  2005 ).

5.5. Risk of bias

As per the Campbell Collaboration reporting guidelines, a risk of bias index was created for the purpose of the present report. The EPOC tool was utilized to assess the risk category of each study on several items relating to the methodological quality of evaluations. Following earlier Campbell review (e.g., Valdebenito et al.,  2018 ) this tool was also used for nonrandomized studies as other risk of bias measurement instruments were considered inappropriate for nonscientific or medical trials.

The following section describes the procedure for addressing risk of bias in the present meta‐analysis. Each primary evaluation was measured on the following items: (1) allocation sequence (AS); (2) Allocation concealment (AC); (3) Baseline equivalence on outcomes (BE); (4) Baseline equivalence on participant characteristics (BC); (5) Incomplete outcome data (ID); (6) Contamination protection (CP); and (7) Selective outcome reporting (SOR). The applicability of these categories for each of the methodological designs included in the present report is outlined in Table  5 . Each study was categorized as being high, low, or unclear (if insufficient information was available) risk on each of these EPOC items.

6. INCLUDED INTERVENTIONS

In total, 67 different school‐based antibullying programs were evaluated by primary studies included in our updated meta‐analysis. Descriptions of each of these interventions is provided in the following section of this report. These narrative reviews of included antibullying programs are based on the best available information provided by the primary studies. Twenty‐one of the evaluated antibullying programs were included (only) in the previous meta‐analysis (Farrington & Ttofi,  2009 ). A number of popular school‐based antibullying programs (n = 7; i.e., Bully Proofing Your School [BPYS], Friendly Schools, KiVa, OBPP, Steps to Respect, ViSC, and Youth Matters) had been re‐evaluated or additional publications since 2009. Hence, the majority of programs evaluated in our updated meta‐analysis ( n  = 40) are new bullying prevention and intervention programs.

The following sections provides detailed summaries of each antibullying program included in our systematic review. Descriptions marked with an * were taken from the previous review (Farrington & Ttofi,  2009 ). To provide the reader with a detailed overview of existing antibullying programs studies subsequently excluded from the meta‐analysis are also included here.

6.1. *Antibullying intervention in Australian secondary schools

This antibullying intervention consisted of several activities that aimed to increase awareness and identification of bullying, to promote empathy for targets of bullying and to provide students with strategies to cope with bullying (Hunt,  2007 , p. 22). The intervention was based on an educational antibullying program, which was delivered by teachers. There was no specific training for teachers. Information about bullying was provided at parent and teacher meetings. Teacher meetings were held in conjunction with regular staff meetings while parent meetings were held after hours. A summary of the information covered at parent meetings was also published in the school newsletter in an attempt to target the wider parent population. Finally, the program includes a 2‐h classroom‐based discussion of bullying (offered by teachers) using activities from an antibullying workbook written by Murphy and Lewers ( 2000 ).

6.2. Anti‐Bullying Pledge Scheme (ABPS)

The ABPS describes a number of local antibullying schemes implemented in UK schools as a result of government recommendations and guidance (Pryce & Frederickson,  2013 ). Schools adopted a declaration of commitment, and intervention components followed a theoretical framework guided by the Theory of Planned Behavior (Ajzen,  1991 ).

The ABPS is a universal prevention program, that aims to reduce the prevalence of bullying perpetration and victimization in schools and increase students' perceptions of safety and support within the school environment (Pryce & Frederickson,  2013 ). Participating schools were assigned a facilitator, referred to as a “pledge supporter,” and a detailed intervention manual. The manual outlined the stages involved in implementing the ABPS program. The stages are as follows:

• Initial meeting with school management and the pledge supporter
• Intervention planning meeting
• School representatives make a declaration of commitment to the intervention
• Staff, student, and parent surveys are circulated
• Results from the surveys were collated and used to tailor intervention components to the individual schools' needs
• Ongoing visits and support from the pledge supporter throughout implementation.

6.3. *Be‐prox program

The Be‐Prox program was specifically designed to tackle bullying and victimization among kindergarten students. According to Alsaker and Valkanover ( 2001 , pp. 177–178), the somewhat higher adult‐children ratio, the interest of preschool teachers in socialization, the greater flexibility as to scheduling and teaching, and the admiration of many preschoolers for their teachers are ideal conditions for the implementation of preventive programs against bully/victim problems. The basic principle of Be‐Prox was to enhance preschool teachers' capacity to handle bully/victim problems (Alsaker,  2004 , p. 291). The program engaged teachers in an intensive focused supervision for approximately 4 months. Central features of Be‐Prox were the emphasis on group discussions, mutual support and co‐operation between consultants and teachers and between teachers and parents (Alsaker,  2004 , pp. 292–293).

The teacher training was provided in six steps (Alsaker,  2004 ; fig. 15.1, p. 292). Initially, teachers were given information about victimization (step 1) and the implications of this information was discussed (step 2). During the third step, specific implementation tasks were introduced and the teachers worked in groups in preparation for the practical implementation (step 4). After this preparation, teachers implemented specific preventive elements in the classroom (step 5) for a specific period of time. After that, teachers met and discussed their experiences of the implementation of the preventive measures (step 6).

In eight meetings over a 4‐month period, issues related to the prevention of bullying were addressed. The main purpose of the first meeting was sensitization. Teachers were asked to describe any possible bully/victim problems in their schools and were then given information about bullying and other types of aggressive behavior. They were also presented with the main principles of the program. The importance of contact between kindergarten teachers and children's parents was also emphasized and teachers were advised to consider the possibility of organizing a meeting with parents. In the second meeting, the importance of setting limits and rules to preschool children was discussed. Teachers were invited to elaborate some behavior codes in their classroom in collaboration with the children and to be ready to present them during the third meeting. Also, as a second homework task, teachers were asked to organize a parent meeting.

During the third meeting, teachers discussed their experiences of implementing classroom rules against bullying. The main focus of this meeting was the need for consistent teacher behavior, the difference between positive and negative sanctioning and the use of basic learning principles in the classroom. The main focus of the fourth session was on the role and responsibility of children who were not involved in bullying and of bystanders in the prevention of victimization. Teachers were asked to draw some kind of personality profiles of passive and aggressive victims and of bullies and to present them to the rest of the group. After this task, teachers were presented with research findings regarding the characteristics of children who were or were not involved in bullying. As a homework task for the next meeting, teachers were asked to systematically observe noninvolved children and to develop some means of involving them in the prevention of victimization.

During the fifth meeting, research‐based information about motor development and body awareness among preschool children was presented to teachers. A discussion between teachers and program researchers of children's self‐perceptions of strength, of peers' perceptions of strengths of victims of bullies, and other motor characteristics of children, aimed to yield important insights. The overall discussion and exchange of information among teachers aimed to promote teachers' understanding about how to change these perceptions within the classroom setting. Specific goals to be achieved within the classroom were clearly set, such as training in empathy and body awareness among children, participation and involvement of noninvolved children and talks with all the children about the situation in their kindergarten. During the sixth meeting, time was given to reflect on the goals formulated at the beginning of the prevention program. Teachers were also given time to discuss their experiences with implementing the goals of the fifth meeting within the classroom settings. The last two meetings followed a similar format, with time given for reflection on goals achieved, problems dealt with, and an overall evaluation of the program.

6.4. *Befriending intervention

Befriending intervention was an antibullying program that relied mainly on a peer support model. The overall aims of the program were: (a) to reduce bullying episodes through developing in bullies an awareness of their own and others' behavior; (b) to enhance children's capacity to offer support to the victims of bullying; (c) to enhance responsibility and involvement on the part of bystanders; and (d) to improve the quality of interpersonal relationships in the class group (Menesini et al.,  2003 , p. 1).

The antibullying intervention was offered in five steps (Menesini et al.,  2003 , p. 5). During the first phase, which targeted the class level (class intervention), several activities were offered aiming to increase children's awareness of prosocial and helping behaviors and to promote positive attitudes toward others. Through work at the class level, the school authorities sensitized and prepared the whole school population for the new service that the school unit was about to implement. In this way, another goal was achieved, namely developing values and attitudes toward “peer support activities” in the whole school population.

During the second phase of the program, the “peer supporters” were selected. Approximately three to four supporters were allocated in each classroom and were selected based on a combination of techniques, such as self‐ and peer‐nominations. These children were then trained in special full‐day sessions or in regular meetings during school time (phase three) so that they knew how to deal with other children and how to facilitate interactions among other children. Teachers and other professionals (psychologists and social workers) took part in these sessions as well. The overall aim of this phase of the antibullying program was to help peer supporters to enhance their listening and communication skills since they would be the mediators in the interactions among children.

During the fourth phase of the program, peer supporters worked in their classes with the assistance and close monitoring of their teachers. The teachers in each class organized “circle meetings” during which the needs of specific children involved in bullying (target children) were identified. Target children were contacted and, after their consent and cooperation, were offered help by the peer supporters. Peer supporters were not only assigned to specific tasks involving the target children but were also supervised by the teachers so that they were given constant feedback on their on‐going work in the class.

During the final phase of the Befriending Intervention, the leading group of peer supporters were involved in training other children in the class, so that more children could be involved in the program (in the transmission of training and passing on the roles).

6.5. *Behavioral program for bullying boys

This program targeted male youth, from a low socio‐economic area, predominately inhabited by individuals of color, involved in bullying. The program was based on the findings of an in‐depth needs assessment within three schools and targeted a specific number of male students aged sixteen who (based on the results of the questionnaire that had been administered) were “considered to be a serious threat to the harmonious functioning of everyday school life” (Meyer & Lesch,  2000 , p. 59). The theoretical basis of the program could be found in the Social Interactional Model for the development of aggression (Meyer & Lesch,  2000 , p. 61) and involved a behavioral approach for tackling the problem of bullying. The program was implemented by psychology students for ten nonconsecutive weeks, with 20‐h‐long sessions held twice weekly at the school, during school hours.

The components of the 17‐session behavioral program included homework tasks, modeling, self‐observation, role‐plays, and a token economy system for reinforcing positive behaviors. According to the program designers “the chief contingency for behavioral change was the token economy system, using Wonderland Games tokens, chocolates and cinema tickets as reward for non‐bullying behavior” (Meyer & Lesch,  2000 , p. 62). Each participant was monitored by himself and by a “buddy” who was selected in each session prior to the monitoring. Each session included an opportunity for feedback on the students' progress in the week, a discussion of a relevant applied topic, role‐playing, games, and drawing. The program designers pointed out the limitations of the intervention strategy. As they indicate (Meyer & Lesch,  2000 , p. 67) “the program was too short and structured to address the issues that were disclosed in sessions, as the severity of the nature of the aggression in the schools and vast social problems was seriously underestimated.”

6.6. Beyond the Hurt

Sutherland ( 2010 ) implemented the Beyond the Hurt program, a peer‐led school‐based bullying intervention and prevention program, developed by the Red Cross. Beyond the Hurt is a high school program and emphasizes education, prevention and intervention to reduce prevalence of bullying perpetration and victimization. Sutherland ( 2010 , p. 84) describes the four key components of the intervention: (1) education and training of peer facilitators, (2) in‐class presentations given by peer facilitators, (3) teacher workshops, and (4) online training material for teachers and community members.

This peer‐led program trains and educates select peer facilitators, who become the implementers of the intervention program within participating schools. These students are guided by a teacher and Red Cross professional throughout training and implementation of class presentations highlighting several bullying‐related issues. The overarching aim of the Beyond the Hurt program is to create a positive school and class climate in which students are encouraged to develop and maintain healthy prosocial relationships, and bullying perpetration and victimization are not supported. The program aims to promote antibullying attitudes among participants and encourage empathy and prosocial support for victims of bullying.

6.7. *Bulli and Pupe

Bulli and Pupe was an intervention program concerned with bullying and family violence. The program, developed by Baldry (2001), was “directed towards the individual and peer group, and aimed to enhance awareness about violence and its negative effects” (Baldry & Farrington,  2004 , p. 3). The intervention package consisted of three videos and a booklet divided into three parts; each video was linked to one part of the booklet. Each part of the booklet was meant to take the form of an interactive lesson where professionals, experienced in school and juvenile processes, discussed three issues according to the structure of the manual.

The first part of the booklet, entitled “Bullying among peers,” emphasized teen violence among peers. The booklet presented vignettes and graphics that reported research findings on bullying in an attempt to raise students' awareness of this issue. The corresponding video showed teenagers talking about bullying based on their own experiences and judgments. The second part of the booklet, entitled “Children witnessing domestic violence,” analyzed the effects of domestic violence on children and the repercussions for school achievement and peer relations. In the accompanying video, children in a shelter for battered women were presented, talking about their personal experiences and emotions. Finally, the third part of the booklet, entitled “Cycle of violence,” dealt with the long‐term effects of violence on adults who were victims of violence in their childhood. The corresponding video consisted of an interview conducted with a 19‐year old boy who had a violent father.

The program was in the first place delivered in 3 days by experts who, together with teachers, discussed about bullying, read the booklet and analyzed its content. The program was taken over by teachers who once a week created a facilitation group and allowed children to discuss any problems they encountered with their peers. The program was more effective with secondary students because it required its participants to have good interpersonal and cognitive skills (Baldry & Farrington,  2004 , p. 4).

6.8. The Bully Prevention Challenge Course Curriculum (BPCCC)

Battey ( 2009 ) implemented the BPCCC (Haggas,  2006 ) to students over two 45 min classes, on 4 days of one school week. The program was implemented by trained facilitators, whom included the schools' physical education/health teacher. The program commenced by providing participants with name tags and organizing some warm‐up physical activities. Next, the physical education/health teacher provided participants with information about bullying, such as, identifying and addressing bullying, who to talk to and where to seek support. Subsequent group discussions focused on empathy and understanding each other's differences. Audience participation activities also required the students to engage to represent the number of students whom had been a victim or bully.

6.9. Bully Proofing Your School

“Bully‐Proofing Your School” was a comprehensive, school‐based intervention program for the prevention of bullying (Menard & Grotpeter,  2014 ; Menard et al.,  2008 ; Toner,  2010 ). The program involved three major components: (1) heightened awareness of the problem of bullying, involving a questionnaire to measure the extent of bullying and the creation of classroom rules related to zero tolerance for bullying; (2) teaching students protective skills for dealing with bullying, resistance to victimization and providing assistance to potential victims by teaching assertiveness skills; and (3) creation of a positive school climate where students were encouraged to work as positive and supportive bystanders (Menard et al.,  2008 , p. 7).

The primary targets of BPYS were elementary and middle school students. School staff were involved as both secondary targets of intervention (since changes in their behavior was a requirement for the construction of a positive antibullying school environment) and as agents delivering the intervention to students. Teachers were given information and strategies to help them recognize bullying incidents among their students and how to effectively deal with these behaviors (Menard & Grotpeter,  2014 ).

The intervention in the classes consisted of a classroom curriculum, which included seven sessions of approximately 30–40 min. Each session was delivered by a teacher or by mental health staff. After completion of the classroom curriculum materials, teachers were encouraged to hold weekly classroom meetings during which students could be helped to reflect on their behaviors. Parents were offered information through newsletters. Individual parents of students involved in bullying as either perpetrators or victims were given consultation (Menard & Grotpeter,  2014 ).

6.10. Chinese antibullying intervention

Ju et al. ( 2009 ) implemented an antibullying program in a Chinese primary school employing an action research framework. There were two main aims of this intervention program. First, the program aimed to reduce bullying perpetration and victimization both on students' way to, and from, school. Second, the study aimed to investigate practical intervention elements that could be applied nationwide to Chinese primary school children (Ju et al.,  2009 ).

The initial step in this intervention was the training of teachers on the fundamental principles of action research. This training program targeted the following components of educational research: (1) research methodology in education; (2) knowledge of school bullying; (3) components of action research; and (4) intervention skills, such as brainstorming and role‐playing. Second, a 5‐week intervention program was designed and implemented by teachers in classrooms. Components that targeted both victims and bullies specifically were also incorporated into the intervention.

6.11. The Confident Kids program

The Confident Kids program is an antibullying intervention designed for early adolescent males who were experiencing anxiety as a result of being bullied at school (Berry & Hunt,  2009 ). The foundations of the program lie in cognitive‐behavioral therapy, employing both anxiety management techniques and antibullying elements. Based on the “Cool Kids Program” (Lyneham et al., 2003), this intervention program aims to reduce bullying victimization by targeting factors that increase the likelihood of victimization. Therefore, this program focuses primarily on issues such as: self‐esteem, coping strategies; social skills; emotional regulation; and internalizing behaviors.

The program was implemented over a period of 8 weeks, and included student and parent involvement. Students participated in weekly group sessions led by a team of assistant and qualified clinical psychologists. These sessions incorporated a combination of tasks including: skill demonstration; role‐playing; and group discussion. Homework was allocated after each session and participants were encouraged to apply skills acquired in real‐life settings between each session.

Sessions covered a variety of issues, including both cognitive‐behavioral anxiety management techniques and antibullying information. Seven core sessions focused on the following topics: psycho‐education; cognitive restructuring (2 sessions); graded exposure; adaptive coping strategies; improving social skills; and self‐esteem. A final session targeted relapse prevention and provided a general overview of the skills learned throughout the program. Parents participated in sessions that ran parallel to the student program. Group discussions targeted the strategies being taught to student participants and also possible parent factors that could influence effectiveness of intervention for their children, for example, parental anxiety.

6.12. Cyberprogram 2.0

Cyberprogram 2.0 is a cyberbullying intervention program that also incorporates elements on school bullying (Garaigordobil & Martínez‐Valderrey,  2015 ). The intervention is delivered over 19 sessions, and outlines the following four main goals:

• To outline and conceptualize bullying and cyberbullying, including identifying the different roles involved (e.g., bullies, victims, and bystanders).
• To illustrate the consequences of bullying and cyberbullying for all those involved
• To develop coping strategies in order to reduce bullying and cyberbullying behaviors.
• Developing positive social and emotional skills, such as empathy, active listening, anger management, conflict resolution strategies, and diversity tolerance.

A wide range of activities and techniques are used, such as, role‐playing, brainstorming, case studies, and guided discussion. The Cyberprogram 2.0 intervention followed a specific methodological framework, employing four key components for implementation. They are as follows: (1) inter‐session constancy: intervention was delivered in weekly 1‐h sessions; (2) spatial‐temporal constancy: intervention was delivered in the same place and at the same time each week; (3) constancy of adult facilitator: intervention was implemented by the same adult, who same psycho‐pedagogical training, each week; and (4) constancy in the session structure: sessions being with group instruction and activities. There is then a following reflection phase that is led by the adult.

6.13. Daphne III

Daphne III was an international antibullying initiative implemented and developed in association with numerous organizations. In this study (Papacosta et al., 2014), school antibullying programs were coordinated in Cyprus by the Association for the Psychosocial Health of Children and Adolescents (APHCA). Other influential “partners” included the Cyprus Ministry of Health, mental health services, Department of Child and Adolescent Psychiatry, Ministry of Education and Culture, and Educational Psychology services. Organizations from other European countries included: Child Line [ Vsi Vaiku Linija ], in Lithuania, and Nicolaus Copericus University, in Poland, were also involved.

The overarching aim of this initiative was to educate 5th and 6th grade primary school students about bullying, and the many different forms it can take (Papacosta et al., 2014). Teachers implemented the program in their classrooms, and were trained by psychology and mental health professionals. There were eleven workshops involved in the program that followed a structured curriculum manual. This manual also provided schools with suggestions and recommendations on ways in with they could prevent, and intervene in, bullying situations.

6.14. *Dare to Care: BPYS program

“Dare to Care; Bully Proofing Your School” was a modification of the “Bully Proofing Your School” program (Beran et al.,  2004 , p. 103), which in turn was modeled on the Olweus Program. This antibullying program placed emphasis on clinical support to victims and perpetrators of bullying in the form of individual and group counseling. It also enabled collaboration with community services. The essence of the program was to encourage accountability for creating solutions among all parties involved in the education system (Beran et al.,  2004 , p. 104).

The program included several steps. Program facilitators provided to school personnel information and training on issues related to bullying in schools (in a full‐day professional development workshop). This workshop aimed to ensure that the program principles would be reflected in the overall curriculum and would be sustained over time. Information was also given to parents. Then, students, parents and school staff collaborated in the development of a school antibullying policy. This policy had the aim of identifying caring and aggressive behaviors and consequences of those behaviors, but with a focus on reparation rather than punishment. The antibullying policy was posted throughout the school. Finally, the program involved the implementation, on behalf of the teachers, of a classroom curriculum that educated children about the nature of bullying and strategies to avoid victimization. The curriculum included discussion, role‐plays, artwork, books, videos and skits presented to school staff, parents, and other children.

6.15. Defeat Bullying

The Defeat Bullying program is a curriculum‐based antibullying program that was published by the National Society for prevention of Cruelty to Children (NSPCC, UK) in 2007 (Herrick,  2012 ). The program materials were available to download online, as part of a nationwide campaign to reduce bullying perpetration and victimization in UK schools. The overarching aim of the Defect Bullying program is to raise awareness and improve attitudes toward bullying, educate about bullying‐related feelings and emotions, and to develop effective intervention and conflict resolution strategies (Herrick,  2012 , p. 85). Based on social identity theory (Tajfel & Turner,  1979 ), the program aims to establish an in‐class antibullying norm, so that students will be encouraged to adopt this norm, and thus, reduce levels of bullying perpetration and victimization.

There are five key lessons implemented throughout the program, and each incorporates a range of individual, class and group activities (Herrick,  2012 ). The lessons cover the following five themes: (1) understanding attitudes and values toward bullying; (2) educating about the feelings that occur as a result of bullying; (3) embracing diversity; (4) safety awareness; and (5) encouraging bystanders to get involved in antibullying strategies. The available intervention materials were also reviewed by groups of teachers, and any necessary amendments were incorporated. For example, Herrick ( 2012 ) describes that following teacher discussion groups, homework assignments relating to each lesson were developed and implemented. Parents of participating students were also invited to attend an antibullying workshop led by the researcher.

6.16. *Dutch antibullying program

The antibullying initiative in the Netherlands was inspired by the Olweus program (Fekkes et al.,  2006 , p. 639). The program was specifically designed to tackle bullying behavior by involving teachers, parents and students. It offered a 2‐day training session for teachers in order to inform them about bullying behavior and to instruct them about how to deal with bullying incidents in schools. During the intervention period, teachers had access to the training staff for additional advice. Intervention schools were supported by an external organization named KPC, which specialized in training school staff and in assisting schools in setting up new curricula and guidelines. The core intervention program included: (1) antibullying training for teachers, (2) a bullying survey, (3) antibullying rules and a written antibullying school policy, (4) increased intensity of surveillance, and (5) information meetings or parents.

During the intervention, there was careful dissemination of the antibullying program to intervention schools. Also, the researchers provided information about the number of intervention and control schools, which have used the above‐mentioned elements of intervention. Finally, intervention schools were supplied with the booklet “Bullying in schools: how to deal with it” and with a “Bullying Test,” a computerized questionnaire that children could complete anonymously in the classroom.

6.17. Dutch Skills for Life

The Skills for Life program is a Dutch universal school‐based behavioral and health prevention program for adolescents aged 13–16 years old (Diekstra,  1996 ; Gravesteijn & Diekstra,  2013 ). The program targets prosocial behavior, self‐awareness, social awareness, self‐control, interpersonal skills, and ethical decision making to reduce behavioral and health problems (Fekkes et al.,  2016 ). The program is based on social learning theory and Rational Emotive Behavioral Therapy. As a result, the program aims to reduce bullying by enabling students to learn from each other in a classroom setting through behavioral modeling.

The program is implemented by teachers, who attend two 3‐day training workshops prior to implementation and receive “booster” training sessions throughout the intervention (Fekkes et al.,  2016 ). The intervention is comprised of 25 lessons that are delivered over the course of two academic years. First, four lessons address awareness and handling of thoughts and feelings. Skills such as interpersonal problem solving, emotional regulation, and critical thinking are targeted. There are twelve additional lessons in the 1st year, and nine more lessons in the 2nd year of implementation. These generally focus on skills that are applicable to particular behavioral or health experiences. For example, lessons are aimed at: dealing with bullying; setting and respecting boundaries; substance use; norms and values; friendships; sexuality; suicidal ideation; and conflicts with peers and/or teachers. Various activities are utilized throughout the program, including, active enactment, DVDs, role play, discussion and feedback.

6.18. Dynamic Approach to School Improvement (DASI)

The DASI (Kyriakides, Creemers, Papastylianou, et al.,  2014 ; Kyriakides, Creemers, Muijs, et al.,  2014 ) was a whole‐school approach to bullying prevention implemented in several European countries, such as: Cyprus, Greece, UK, Belgium and the Netherlands. This approach draws factors from the educational effectiveness model (Creemers & Kyriakides,  2008 ,  2012 ). The intervention targets specific school factors, that is, (1) school teaching policy, (2) school learning environment, and (3) school evaluation. This framework was previously found to improve academic achievement (e.g., Kyriakides,  2008 ).

At the beginning of the intervention, the research team held training for participating school staff. The theoretical framework was introduced, and a detailed manual was provided. The aim of the handbook was to facilitate school stakeholders to develop strategies and action plans that were specific to the schools' needs (Kyriakides, Creemers, Papastylianou, et al.,  2014 ). Support was offered to each school by the research team throughout the process.

Teacher surveys were distributed prior to implementation in order to highlight specific areas that needed improvement. The next phase of the intervention involved school stakeholders coming together to form cooperative committees with representatives of parents, students, and teachers. These committees then collaborated to develop action plans and strategies to address specific problems in their schools. Committees formulated plans to implement particular intervention components that best suited their specific needs. Therefore, the schools participating did not necessarily implement the same intervention components or activities. Schools were required to retain log books of activities undertaken.

Kyriakides, Creemers, and Papastylianou, et al. ( 2014 ) provide an outline of the intervention components implemented in one experimental school involved in their trial. For example, the following are identified as essential elements implemented in order to reduce bullying:

• “Student behavior outside the classroom”—involves developing clear and efficient antibullying policy, increased teacher vigilance in bullying “hot spots” and effective supervision of students.
• Improved school learning environment
• “Rewarding good behavior”—enforcing a system that acts as a nonpunitive approach to antibullying, by motivating students to behave in a prosocial manner.
• “Collaboration and interaction between teachers”—encouraging teachers to work together and communicate effectively about bullying issues in their schools.
• Other intervention components, including, encouraging and supporting peer bystanders; identifying and support “at risk” and vulnerable students; and creating student‐made videos about bullying issues.

6.19. *Ecological antibullying program

The Ecological antibullying program examined peer group and school environment processes “utilizing a systemic interactional model with evaluations at each level of intervention” (Rahey & Craig,  2002 , p. 283). The overall aim of the program was the creation of a supportive and safe school environment in which firm limits against bullying were established. The specific goals of the program included raising awareness of the problem of bullying, increasing empathy, encouraging peers to speak against bullying and formulating clear rules against bullying.

The 12‐week program was based on the “Bully Proofing Your School” program which was designed to increase the understanding of bullying and decrease the incidence of bullying (Rahey & Craig,  2002 , p. 285). The program elements included a psycho‐educational component implemented within each classroom, a peer mediation component and specialized groups for children involved in bullying.

At the school‐wide level, the psycho‐educational program was implemented by psychology students who received training sessions and manuals prior to intervention. Prior to the program, at a school assembly the program was introduced to students. The assembly signaled the formal beginning of the intervention. The classroom programs involved interactive educational approaches such as role playing and puppet techniques. The topics addressed were bullying and victimization, conflict resolution, empathy, listening skills and individual differences (Rahey & Craig,  2002 , p. 286).

Individual programs for children involved in bullying were also part of the intervention. The relevant sessions consisted of social skills, listening, empathy training and supportive counseling. Each weekly session lasted 45 min. The program also included intervention at the teacher level. Teacher programs consisted of meetings with teachers to discuss bullying, intervention approaches, and student support for those directly involved in bullying. During the intervention, the program coordinators met with principals and teachers to offer support.

6.20. Emotional Literacy Intervention

Knowler and Frederickson ( 2013 ) evaluated the effectiveness of an emotional literacy intervention targeted on bullying behaviors to reduce bullying victimization in UK schools. Selected schools were previously implementing the Social and Emotional Aspects of Learning (SEAL; Department for Education and Skills,  2005 ) program. One of the themes included in the SEAL program is “Say no to bullying” (Knowler & Frederickson,  2013 ), however the overall program aims to improve students' social relationships, motivation, learning strategies, and holistic school improvement.

The specific emotional literacy intervention implemented and evaluated by Knowler and Frederickson ( 2013 ) involved teaching emotional literacy skills to small groups of students (Faupel,  2003 ). In the current evaluation, the intervention was delivered to groups of “low emotional literacy” and “high emotional literacy” groups distinguished by scores above, or below, median scores on the Emotional Literacy assessment‐pupil form (ELA‐PF; Faupel,  2003 ). The intervention program employed 12 weekly lessons and was implemented by trained teaching aids (Knowler & Frederickson,  2013 ). The program consisted of four main concepts: (1) self‐awareness, (2) self‐regulation, (3) empathy, and (4) social skills. Lessons employed a variation of behavioral and cognitive‐behavioral elements (Faupel,  2003 ).

6.21. Empathy training program

This intervention program was developed for children identified as bullies and aimed to increase their empathetic skills in order to reduce their bullying behaviors (Şahin,  2012 ). The empathy training program was implemented over eleven 75‐min sessions that were based on a curriculum lesson plan developed by the author. Several cognitive techniques were utilized throughout the program, such as: recognizing, evaluating and naming feelings; diadtic, experimental, modeling and role‐playing, in order to improve the students' cognitive abilities in relation to empathy. Each lesson required the students to work together to develop a slogan that emulated the content of the session. The following is an outline of the first 4 weekly lessons, and the associated slogan developed, (for a full outline see: Şahin,  2012 , p. 1327; Table  2 ).

Slogan: Be kind, loving and forgiving to each other to lead a happy life .

Slogan: Living without the awareness of feelings is like driving a car with its brakes on .

Slogan: One who claims to know everything about the universe but nothing about himself, actually knows nothing .

Slogan: We can look at the same thing but view it differently .

6.22. *Expect respect

Expect Respect was a school‐based program that aimed to promote awareness and effective responses to bullying and sexual harassment. The project was developed by Safe Place, the sole provider of comprehensive sexual and domestic violence prevention and intervention services in Austin, Texas (Rosenbluth et al.,  2004 , p. 211). The program targeted the involvement of all members of the school community in recognizing and responding to bullying and sexual harassment. The overall project design was inspired by the work of Olweus (Rosenbluth et al.,  2004 , p. 212). Expect Respect consisted of five core program components, namely a classroom curriculum, staff training, policy development, parent education and support services.

The classroom curriculum was based on 12 weekly sessions adapted from a specific manual called “Bullyproof: a teachers” guide on teasing and bullying for use with fourth and fifth grade students' (Whitaker et al.,  2004 , p. 330). The Bullyproof curriculum was designed to be taught in conjunction with literature typically read by fourth and fifth graders. Although the antibullying curriculum was designed to be implemented by teachers, within the framework of the Expect Respect program, it was jointly led by Safe Place Staff and teachers or school counselors (Whitaker et al.,  2004 , p. 331). The curriculum aimed to increase the ability and willingness of bystanders to intervene in bullying situations, thus reducing the social acceptability of bullying and sexual harassment. The Bullyproof lessons included writing assignments, role‐plays of how to intervene in bullying situations, class discussions and so on.

With regard to the staff training, a 6‐h training was provided to project staff, counselors, and fifth grade teachers. The training was given by the author of the specific manual and aimed to prepare school personnel to respond effectively to bullying incidents. In addition, 3‐h training sessions were provided once per semester for all personnel, including bus drivers, cafeteria workers, hall monitors and office staff. The training presentation included research on bullying and sexual harassment; strategies to enhance mutual respect among students; practice in using lessons from the curriculum; and methods for integrating the lessons into other subject areas including language arts and health.

School administrators were encouraged to develop an antibullying policy (policy development) in their school to ensure consistent responses by all staff members to incidents of bullying and sexual harassment. Principals were expected to present the policy to school staff, students and parents. In order to facilitate the overall procedure of policy development, Expect Respect staff provided an initial policy template to school administrators (Whitaker et al.,  2004 , p. 332) and each school was encouraged to expand this initial policy in accordance with the specific needs of their unit.

The Expect Respect program also included parent training. Educational presentations were offered to parents, twice a year, providing information about the project. The information given to parents through these meetings (as well as through parent newsletters sent home) was aimed at enhancing parents' strategies to help children involved in bullying as bullies, victims, bully‐victims, or bystanders.

Further support services were provided such as continuous assistance of school counselors by Safe Place staff. School counselors were given a specialized session on how to deal with students who were repeatedly involved in bullying as either perpetrators or victims. They were also provided with a comprehensive resource manual containing reading and resource materials on bullying, sexual harassment and domestic violence.

6.23. fairplayer.manual

The fairplayer.manual is a structured, curriculum‐based antibullying program for Grade 7–9 students (Bull et al.,  2009 ; Wölfer & Scheithauer,  2014 ). The overarching aim of the intervention is to reduce bullying and relational aggression by improving students' social and moral competencies. The program focuses on raising awareness, changing attitudes, and encouraging bystander intervention.

The program is implemented over 15‐weekly 90 min lessons, and can be delivered either by trained teachers (Bull et al.,  2009 ), or psychologists (Wölfer & Scheithauer,  2014 ). Intervention lessons employ cognitive‐behavioral techniques and target nine specific topics. The first introductory lesson introduces the program to students, and class antibullying rules are developed. Two following lessons are concerned with raising awareness about bullying‐related issues, such as, the various forms of bullying and the consequences associated with perpetration and victimization. One lesson subsequently focuses on improving students' understanding of their own and peers' feelings. A further two lessons highlight the numerous participant roles involved in bullying, for example, bullies, victims, outsiders (i.e., noninvolved), assistants, and re‐inforcers (Wölfer & Scheithauer,  2014 ). The latter roles describe different forms of bystanders, those who witness bullying and allow it to happen and those who reinforce bullying behaviors. Social dynamics in the classroom is also addressed in one intervention session. By addressing the different dynamics, networks and norms socially in the class, this lesson aims to improve the classroom climate and encourage co‐operation among students. Another intervention lesson models and promotes bystander intervention in order to encourage noninvolved children to become actively engaged with intervening in bullying situations that they may witness.

Following these core awareness‐raising and knowledge‐improving lessons, participating students undertake five social skill‐training session s. These lessons focus on developing social, emotional, and moral skills of participants, in order to combat bullying. Perspective taking, empathy, and moral dilemmas are just some of the issues that are included. Diversity is the topic addressed in one of the following lessons, where students learn to respect and appreciate diversity. Finally, a concluding lesson brings together all of the issues covered by the intervention and demonstrates ways in which participants can utilize skills and knowledge in their everyday lives.

6.24. FearNot!

The FearNot! (Fun with Empathetic Agents to achieve Novel Outcomes in Teaching; Sapouna et al.,  2010 ) was an immersive learning intervention that aimed to reduce bullying victimization. Students from British and German primary schools participated in the virtual learning program for weekly 30‐min sessions over the course of three consecutive weeks. Participating schools were required to have adequate computer facilities in order to be able to run the program.

During intervention sessions bullying scenarios were enacted by male and female 3D animated characters. The content of these scenarios reflected the characters' genders, for example, scenarios involving male characters included more incidents of physical bullying, whereas female characters demonstrated more relational bullying. Following each of the bullying episodes, participants were asked to interact and provide the animated victim of bullying with a suitable coping strategy to prevent future victimization. The program then enabled students to see the outcomes of their suggested strategy. In some circumstances, the animated victim of bullying responded that they did not feel emotionally adequate enough to carry out the suggested coping strategy (e.g., not strong enough to stand up to the bully).

Based on previous research (e.g., Kochenderfer & Ladd,  2000 ), students were then provided with an indication of how successful their proposed coping mechanism would be in real‐world bullying scenarios. For example, students were provided with a score on a scale of zero (never successful) to ten (always successful; Sapouna et al.,  2010 ). In addition to the computerized program, teachers in intervention schools were provided with a detailed intervention manual. However, during the FearNot! program, teachers were instructed only to assist students with issues of comprehension, and not to guide them on suitable responses to the bullying scenarios.

6.25. Fourth R

The Fourth R: Strategies for Healthy Youth Relationships is a dating violence prevention program that targeted bullying perpetration and victimization as secondary outcomes (Cissner & Ayoub,  2014 ). This curriculum‐based intervention program was based on social learning theory (Bandura,  1978 ), and was implemented in classrooms by trained teachers during health and physical education classes. Participating teachers completed an intensive 1‐day training session that provided them with the skills to implement the program effectively. Detailed manuals and lesson outlines/materials were provided, and the Fourth R curriculum was integrated into existing health and physical education curricula.

The Fourth R was designed as a 21‐lesson curriculum that incorporates a variety of activities and lessons. Role‐playing, individual, pair and group work, and detailed examples/scenarios of conflict are examples of Fourth R‐style tasks. Program lessons were categorized into the following 3 units: (1) Personal Safety and Injury Prevention; (2) Healthy Growth and Sexuality; and (3) Substance Use and Abuse. Each unit consisted of seven 45‐min lessons. The Fourth R was also designed to be implemented in either gender‐segregated or co‐ed classrooms.

6.26. *Friendly Schools Project

“Friendly Schools” was a theoretically grounded program. Its educational techniques (e.g., role modeling, drama activities, skills training, etc.) were based on notions derived from Social Cognitive theory, the Health Belief Model and Problem Behavior theory (Cross et al.,  2004 ,  2011 ). An interesting aspect of this program is that it was based on the results of a systematic review (Cross et al.,  2004 , p. 187), which provided a set of key elements to be included in the final intervention strategy. The program targeted bullying at three levels: (a) the whole‐school community, (b) the students' families, and (c) the fourth and fifth grade students and their teachers.

With regard to the whole‐school intervention component, in each school, a Friendly Schools Committee was organized with key individuals (e.g., a parent representative, a school psychologist, a school nurse, teaching staff) who could co‐ordinate and successfully sustain the antibullying initiative. Each committee was provided with a 4‐h training, designed to build members' capacity to address bullying. Each member was provided with a specific strategy manual. The manual was a step‐by‐step guide on how to implement the antibullying initiative. It included among others the Pikas “Method of Shared Concern” and the “No Blame” approach (Cross et al.,  2011 ; Pikas,  2002 ).

With regard to the family intervention component, this included home activities linked to each classroom‐learning activity. Parents were also provided with 16 skills‐based newsletter items (eight for each year of the intervention) that aimed to provide research information on bullying as well as advice to parents on what to do if their child was a perpetrator or a victim of bullying behavior.

Moving on to the Grade 4 and 5 classroom curricula, the Friendly Schools curriculum consisted of nine learning activities per year. The curriculum was offered by trained teachers in three blocks of three 60‐min lessons, over a three‐school‐term period. The learning activities aimed to promote awareness of what was bullying behavior; to help students to become assertive and talk about bullying with teachers and parents; and to promote peer and adult discouragement of bullying behavior.

Finally, the Friendly Schools program offered manuals to teachers. The teacher manuals were designed to be entirely self‐contained so as to maximize the likelihood of teacher implementation. Friendly Schools project staff also provided teacher training (a 6‐h course) for all intervention teachers.

6.27. *Granada antibullying program

This program was a pilot antibullying program with the following aims: (a) to establish children's involvement in bullying within different participant roles/categories; (b) to reduce the number of students involved in the phenomenon as bullies, victims and bully‐victims; (c) to increase the number of students who are categorized as noninvolved in bullying, through the enhancement of prosocial skills; and (d) to identify the threats to fidelity of the program and establish the validity of the pilot program with the possibility of replicating it in future (Martin et al.,  2005 , p. 376). Forty‐nine sixth graders from one Spanish primary school in Granada participated in the program.

The program designers gathered information about the social, educational and economic background of the school, of the students' families and the community in general. That was done during 3 meetings/seminars of 3 h each. Parents, teachers and members of the educational team attended those meetings. Through these meetings, it was established that the program should target interpersonal relationships of the children. It was decided that the program would be curriculum‐based as part of the normal program of the school. It was decided that the program would be implemented by one of the researchers because the teachers did not have enough qualifications to do it and because of lack of time and resources for teacher training. Parents and teachers were provided with information about bullying (a dossier/file) that they could use to discuss the problem of bullying with children. Also, teachers could attend the intervention program so that later they would be able to implement it by themselves. Parents were invited to attend some talks on bullying that would be given by the implementation team so that the program could be continued outside the school. The program was implemented for 5 months at the classroom level (30 sessions; 3 sessions per week with one tutor, i.e., one of the evaluators).

During the first 5 sessions, the tutor informed the children about peer bullying. Topics covered in the first 5 sessions involved issues such as concept of bullying, types of bullying, how to identify it, individual and group differences in bullying, and classroom rules against bullying. From the 6th to the 21st sessions, the program emphasis was on the emotional and social abilities of the children. Several topics were covered such as: identification and expression of emotions during bullying situations; communication abilities; ability to pose questions; ability of children to give and receive complements and complaints; ability to say no in life; ability to ask for a change of behavior; and ability to solve interpersonal problems. From the 17th to the 21st sessions, the program placed emphasis on mediation.

From the 22nd to the 25th sessions, the program emphasis was on human rights. Several topics were covered such as: freedom and equality, respect of private life, respect for other people's belongings, and respect for others' opinions. Similarly, from the 26th to 30th sessions, the emphasis was on moral education. During the whole program (sessions 1–30), there was also an emphasis on the inhibition of impulsivity and enhancement of reflexivity. For the enhancement of reflexivity, the program designers used a specific program called “Programa de Intervencion para Aumentar la Attention y la Reflixividad” [PIAAR] developed by Gargallo (2000) (see Martin et al.,  2005 , p. 378). This focuses on cognitive techniques that aim to inhibit impulsivity and enhance self‐control. The program also included role‐playing, peer mediation, guided discussion, brainstorming, and drawings.

The authors acknowledge several problems with the implementation of the program such as: little involvement by parents and teachers; implementation of the program lessons during recess time or during the physical education program; lack of time to cover all the topics; no second follow‐up because of difficulties of following the children; problems with the size and selection of the sample; the instrument they used; and possible contamination of results because of the way they categorized the children (Martin et al.,  2005 , p. 382). These pitfalls could easily be spotted. For example, the evaluators indicate that they implemented the program with the most aggressive sixth graders who had the worst interpersonal problems (Martin et al.,  2005 , p. 738). This made it difficult to know whether any changes in bullying in the experimental condition were attributable to the effectiveness of the program or to regression to the mean. Also, even though they distributed a self‐report questionnaire, they categorized children based on those questionnaires only after teachers' suggestions.

6.28. *Greek antibullying program (1)

The Greek antibullying initiative was a 4‐week intervention program that aimed to minimize both bullying and victimization. The conceptual framework of the Greek antibullying program was based on the theoretical model proposed by Salmivalli in 1999 (Andreou et al.,  2007 , p. 696), according to which changing an individual's behavior (e.g., the bully's behavior) entailed motivating not only the particular person but also the rest of the group members (participant roles' approach).

The program was embedded within the wider curriculum of the fourth‐, fifth‐, and sixth‐grade classrooms and consisted of eight instructional hours, each hour corresponding to one curricular activity. The curricular activities were presented to students by their classroom teachers who received training beforehand. The teacher training consisted of five 4‐h meetings and aimed to increase awareness of the bullying problem and its seriousness as well as to raise teachers' self‐efficacy in implementing the program (Andreou et al.,  2007 , p. 697).

The Greek antibullying curriculum was divided into three parts in accordance with the three main theoretical axes proposed by Salmivalli in 1999, namely: (1) awareness‐raising; (2) self‐reflection; and (3) commitment to new behaviors (Andreou et al.,  2007 , pp. 697–698).

In line with the first axis (awareness‐raising), small‐group and whole‐class discussions were conducted (over three instructional hours) that aimed to increase students' awareness of the bullying problem. Corresponding materials included a real snap‐shot from the playground, a story entitled “A new friend” and students' own drawings. In line with the second theoretical axis (self‐reflection), two instructional hours involving classroom discussions were conducted. These discussions placed emphasis on the participant roles that students took in the bullying process. Corresponding materials involved each students' completion of open‐ended sentences. Through this activity students were intended to reflect on critical issues around the causes, benefits, feelings, and consequences of adopting different roles. In line with the final axis (commitment to new behaviors), three instructional hours of small‐group and whole‐class discussions were conducted concerning different ways of approaching or solving the peer‐conflict situation and the formulation of class rules. Corresponding materials involved an open‐ended comic‐strip for group completion to find a solution to the bullying situation presented in the relevant story.

6.29. Greek antibullying program (2)

This antibullying program was implemented in Greek elementary schools during the academic year 2011/2012 (Tsiantis et al.,  2013 ). The school‐based program incorporated many elements and was implemented by teachers. Participating teachers attended a 2‐day training seminar before implementation began. A teacher's manual (Tsiantis,  2011 ) was also provided and outlined the detailed and systematic procedures involved in the intervention. Throughout the program teachers were provided with additional support from two mental health professionals whom acted as program co‐ordinators.

The program comprised of 11 weekly workshops that were implemented for two 45‐min class periods (90‐min in total). Class activities included group discussions, games and the formation and signing of class antibullying rules (Tsiantis et al.,  2013 ). Parent meetings were also organized to increase parent participation with the intervention. The first meeting provided parents with information about the intervention program and bullying issues. During the second parent session, students presented the achievements they had made during the intervention.

6.30. Inclusive

The INCLUSIVE program is a whole‐school restorative approach to bullying prevention and intervention (Bonnell et al.,  2015 ). The program involves creating an “action group” within each participating school in order to combat bullying. These groups are comprised of a minimum of six students and six members of staff, with at least one representative from senior management, teaching, support, and pastoral staff. Each action group is appointed an external expert facilitator for the duration of the intervention. It is the facilitators' role to provide ongoing support and training to each member of the action group. Action groups were required to meet regularly throughout the intervention year, approximately once every half term.

The INCLUSIVE intervention was designed to include several core standardized intervention components, including staff training in restorative practices, and a student social and emotional skills curriculum. However, the program also allows for schools to adapt the intervention according to school‐specific needs. These needs were established using a needs assessment survey distributed to year 8 students prior to commencement of the intervention. This survey aimed to establish student views on bullying and aggression in their schools, while providing information regarding school engagement and connectedness, perceptions of safety/risks, social support and social skills, relationships, and teaching in personal, social and health (PSHE) classes. Results of the needs assessment survey were then employed by the action group to tailor the INCLUSIVE intervention to target specific needs. The action groups also utilized this information to review and improve schools' existing policies, procedures and schemes (e.g., peer mediation and “buddying” schemes).

In relation to the core components of the INCLUSIVE intervention, all school staff were provided with introductory training in restorative practices by their affiliated expert facilitator. A minimum of twenty school staff were also required to attend intensive training provided by a specialist training provider. Restorative practices, such as “Circle Time,” were taught to staff to improve school climate and student‐staff communication. This technique involves teachers and staff sitting together in a circle discussing various emotional, social, and curricular issues. Each member of the circle is considered a valued contributor, and all inputs are treated equally. Circle time aims to support student communication and promote positive relationships. Another restorative technique used in the INCLUSIVE program was “formal conferencing,” which aimed to deal with serious bullying and aggressive incidents directly. Formal conferencing involves bringing together teachers, parents and students to establish appropriate punishment and ways in which the harm caused can be repaired. This approach emphasizes a nonjudgmental and inclusive environment so that both victims and perpetrators of bullying and/or aggression are involved.

Year 8 students also completed 5–10 h of social and emotional skills training throughout the process of the INCLUSIVE intervention. These lessons were based on the Gatehouse Project curriculum and could be delivered as either stand‐alone modules or integrated into existing academic curriculums. Modules covered included: (1) Establishing respectful relationships; (2) Emotion management; (3) Understanding and creating trusting relationships; (4) Exploring others' needs and avoiding conflict; and (5) Maintaining and repairing relationships.

6.31. *KiVa

The name of this project is an acronym of the expression “Kiusaamista Vastaan” which means “against bullying.” The word “kiva” in Finnish means “nice” and this is why this acronym was chosen for the specific antibullying initiative in Finland. Regarding the overall perspective of the program, the KiVa project included a universal and an indicated intervention (Kärnä et al.,  2011a ,  2011b ,  2013 ; Nocentini & Menesini, 2016; Salmivalli et al., 2007). The universal intervention referred to efforts made to influence the group norms while the indicated intervention referred to the way in which specific cases were handled in schools through individual and group discussions between the teacher and the students involved (Salmivalli et al., 2007, p. 6).

The KiVa program included a large variety of concrete materials for students, teachers, and parents. It also utilized the Internet and virtual learning environments (e.g., computer games against bullying) aiming in this way to enhance students' attitudes against bullying. Also, students received their own personal user ID, which they could use as a password before the completion of each web‐based questionnaire on bullying. KiVa included 20‐h student lessons, which were carried out by student teachers. The lessons involved discussions, group work, short films about bullying, and role‐playing exercises. After each lesson, a class rule was adopted, based on the central theme of the lesson.

A unique feature of the KiVa program was the use of an antibullying computer game. The game involved five levels and the teacher always activated the next level of the game after the relevant lesson was completed. Students were able to begin using the game after the third lesson; the second level of the program was played after the fifth lesson, and so on until the end of the school year. Each level of the computer game included three components that were named as “I know,” “I can,” and “I do.” In the first component, students were informed about basic facts on bullying. In the second component, the “I can”‐component, students moved around in the virtual school and faced different challenging bullying incidents. Finally, the third component was used to encourage students to make use of their knowledge and skills in real life situations.

Another important element of the KiVa project was the teacher training. Teachers were also provided with vests that they could use during playtime while supervising the school yard. This simple technique aimed to enhance teachers' visibility in the schoolyard and to signal that bullying was taken seriously in the school. Also, all teachers carrying out the KiVa program could seek advice from a web‐based discussion forum, where they could share experiences and ideas about bullying with other colleagues.

Within the school framework, the program also facilitated the use of a peer support group for victims of bullying. The classroom teacher was expected to arrange a group with 2–4 classmates—those who were pro‐social and had high status in the class—who were expected to provide support to victimized students, thus sustaining healthy peer relationships. An interesting element in the KiVa program is that it incorporated both punitive and nonblame approaches when dealing with perpetrators of bullying. Half of the school teams were instructed to use more punitive approaches (e.g., what you have done is wrong and it has to stop right now) while the rest of the school teams were instructed to use no‐blame approaches in their discussions with children (e.g., “your classmate is also having a hard time and this is why he behaves like that; what could we do to help him?”). There was also co‐operative group work among experts when dealing with children involved in bullying.

Finally, the KiVa program involved parents. A parents' guide was sent to the home and provided information about bullying and advice on how parents could be involved to reduce this problem. Information nights for parents were also organized and provided.

6.32. Lead Peace Intervention

The Lead Peace intervention is based on a resiliency conceptual framework (Resnik,  2000 ), thus, aims to reduce youth problem behaviors using an assets‐based approach (Harpin,  2011 ; Sieving & Widome,  2008 ). The intervention was developed as a school‐based “service learning and health education” program to reduce risk of violence and school failure in middle school students (Sieving, 2006). Developed from the Points of Light Youth Leadership curriculum for 9th to 12th grade students (Sieving, 2006), the program was adapted for use with Grade 6–8 students (Harpin,  2011 ).

The core curriculum targets factors on three levels: (1) environmental (e.g., adult resources and supports, family norms and behaviors, peer norms and behaviors, school/community opportunities and social connectedness); (2) personal (e.g., attitudes, beliefs, perceived norms, emotional distress); and (3) behavioral (e.g., social and emotional skills, coping behaviors, school performance). The program aims to reduce risky health and social behaviors (e.g., interpersonal aggression, physical fighting, bullying) in order to promote positive and reduce risky behaviors. The curriculum is implemented for 3 years, and can be delivered in two “doses”: (1) Lead Peace program (basic)—includes 15–20 intervention lessons each year; or (2) Lead Peace plus program—includes 30 intervention lessons, 15–20 additional community service hours, and health education and family outreach activities.

6.33. Lunch Buddy Mentoring program

The Lunch Buddy mentoring program was a school‐based antibullying program that aimed to reduce bullying victimization in elementary school children (Elledge et al.,  2010 ). The program was based on previous research that suggests youth mentoring can be utilized as an effective prevention technique (Dortch, 2000). In comparison to peer‐mentoring antibullying program, the Lunch Buddy program employed college student mentors based on prior success of college student mentoring aggressive children (Cavell & Hughes,  2000 ).

Mentors were provided with training prior to implementation of the program and participated in weekly meetings throughout the program. Children were identified as potential participants using a self‐ and teacher‐report victimization index. The self‐report School Experiences Questionnaire (Kochenderfer & Ladd,  2000 ) and teacher ratings of child victimization due to physical, verbal and relational aggressive were combined to create this index. School principals also collaborated with counselors to identify potentially suitable candidates. Eligible participants were then matched with same‐sex college student mentors, based on the availability of mentors during the mentees scheduled lunchtimes. Mentors visited the mentees twice a week, over the course of 5–6 months. During these visits mentors were required to sit with their mentee and their peers during lunchtime. Each mentor was also required to complete a log sheet after each visit.

6.34. Media Heroes

Chaux et al. ( 2016 ) evaluated the effectiveness of the cyberbullying prevention program “Media Heroes” [ Medienhelden ] on reports of traditional school bullying. The Media Heroes program is based theoretically on the Theory of Planned Behavior (Ajzen,  1991 ) and the social context of participant roles in bullying (Salmivalli,  2010 ). The program aims to reduce cyberbullying perpetration by enhancing empathy, increasing awareness and knowledge about what constitutes cyberbullying, the safety risks associated with Internet activity, and by providing assertive and useful methods in which bystanders can intervene in cyberbullying (Chaux et al.,  2016 ).

There are two versions of Media Heroes: (1) a short version implemented over four 90‐min lessons that take place in one school day; and (2) a long version that is implemented over 15‐weekly 45‐min lessons (Schultze‐Krumbholz et al.,  2012 ). Intervention activities include, role‐playing, class debates, news and film content, group learning and student‐parent presentations (Chaux et al.,  2016 ). Measures of both traditional‐ and cyber‐bullying were implemented in this evaluation, due to the significant overlap in the prevalence of these behaviors.

6.35. NoTrap!

Noncadiamointrappola (Let's Not Fall into a Trap), or NoTrap!, is a web‐based antibullying program that has been developed, implemented and refined over several studies (Menesini et al.,  2012 ; Palladino et al.,  2012 ,  2016 ). Initially implemented in two Italian schools in 2008, the program involves students actively engaging in the development of a website promoting antibullying (Menesini et al.,  2012 ). A selected number of students per school are provided with training and enroll as online peer‐educators. These students acted as online moderators of an antibullying forum, regulating discussion threads and responding to users' questions and concerns (Menesini et al.,  2012 ). In addition, peer‐educators also conducted face‐to‐face awareness raising workshops and meetings with their classmates, to highlight the key issues surrounding traditional‐ and cyber‐bullying (Palladino et al.,  2016 ).

Subsequent editions of the NoTrap! program incorporated additional elements based on findings from previous evaluations. For example, Palladino et al. ( 2012 ) placed more emphasis on: (1) victims' roles and victim support, (2) involving bystanders, (3) greater involvement of teachers in antibullying activities, and (3) creation of a Facebook group to supplement online materials. The third revision of the NoTrap! program incorporated standardization of the face‐to‐face antibullying activities led by peer educators (Palladino et al.,  2016 ). New peer‐led activities involved group work that targeted empathy and problem‐solving skills (Palladino et al.,  2016 ).

6.36. *Olweus Bullying Prevention Program

The OBPP was a multilevel program aiming at targeting the individual, the school, the classroom and the community level. Apart from marked mass‐media publicity, the program started with a 1‐day school conference during which the problem of bullying was addressed between school staff, students, and parents. This signaled the formal commencement of the intervention. Two different types of materials were produced: a handbook or manual for teachers (entitled “Olweus” core program against bullying and antisocial behavior') and a folder with information for parents and families. The program also included: (1) CD‐program that was used for assessing and analyzing the data obtained at the pre‐test period, so that school‐specific interventions could then be implemented; (2) a video on bullying; (3) the Revised Olweus Bully/Victim Questionnaire and (4) the book “Bullying at school: what we know and what we can do.”

The antibullying measures mainly targeted three different levels of intervention: the school, the classroom and the individual. At the school level, the intervention included:

• Meetings among teachers to discuss ways of improving peer‐relations; staff discussion groups.
• Parent/teacher meetings to discuss the issue of bullying.
• Increased supervision during recess and lunchtime.
• Improvement of playground facilities so that children have better places to play during recess time.
• Questionnaire surveys.
• The formation of a coordinating group.

At the classroom level the intervention included:

• Students were given information about the issue of bullying and were actively involved in devising class rules against bullying.
• Classroom activities for students included role‐playing situations that could help students learn how to deal better with bullying.
• Class rules against bullying.
• Class meetings with students.
• Meetings with the parents of the class.

At the individual level the intervention included:

• Talks with bullies and their parents and enforcement of nonhostile, nonphysical sanctions.
• Talks with victims, providing support and providing assertiveness skills training to help them learn how to successfully deal with bullying; also, talks with the parents of victims.
• Talks with children not involved to make them become effective helpers.

An interesting feature of the OBPP is that it offered guided information about what schools should do at both the intervention and the maintenance period. The Olweus program demands significant commitment from the school during the 'introductory period' which covers a period of about 18 months. Later the methodology acquired by the staff and the routines decided by the school may be maintained using less resources … Yet, even for the maintenance period, the program offers a point by point description of what the school should do to continue its work against bullying in accordance with Olweus methodology (Olweus, 2004c, p. 1). Also, at the school level training was offered to the whole school staff, with additional training provided to the coordinators and key personnel. These were responsible for coordinating the overall antibullying initiative in their school. The program also included cooperation among experts and teachers (e.g., psychologists) who worked with children involved in bullying.

6.37. Positive Action program

The Positive Action Program is a generalized school‐based “well‐being” program (Lewis et al.,  2013 ). The program targets both distal (e.g., school climate and teacher classroom management) and proximal (e.g., students' thoughts, feelings, and self‐efficacy) facets are targeted in order to impact a range of health‐ and behavioral‐related outcomes (Li et al.,  2011 ). The program is based on three core elements.

First, the Positive Action philosophy. Based on the theory of self‐concept (Combs,  1962 ; Purkey,  1970 ; Purkey & Novak,  1996 ) and a Positive Psychology (Frederickson, 2000; Seligman & Csikszentmihalyi,  2000 ) approach, the philosophy emphasizes positive feelings about the self, to encourage positive behaviors toward others (Flay & Allerd,  2010 ). Second, the Thoughts‐Actions‐Feelings Circle concept is used throughout the program to illustrate the reinforcing cycle of thoughts, feelings and actions. This is delivered to outline that positive thoughts lead to positive actions, positive actions in turn lead to positive feelings, which then reinforce positive thoughts. Third, a strict six‐unit curriculum that involves daily lessons, interactive learning and social‐emotional skill development.

The PA curriculum is designed to be adapted for kindergarten to Grade 12 students, and is based on six key concepts: (1) self‐concept; (2) social and emotional positive actions for managing oneself responsibly; (3) positive actions relating to a healthy body and mind; (4) honesty with oneself; (5) getting along with others; and (6) continuous self‐improvement (Lewis et al.,  2013 ). The intervention program also involves teacher, parent/family and community training. Schools implementing the PA program receive support from developers throughout implementation by training, manuals, school‐wide climate development, counselors, family classes, and individual consultations for staff with a PA implementation coordinator.

6.38. Preventure and Adventure CBT

The Preventure and Adventure intervention programs were part of two 2 year longitudinal projects that targeted adolescent alcohol use and bullying behaviors (Topper,  2011 ). Intervention components were primarily personality‐targeted cognitive behavioral therapy (CBT) for “high risk” students. Participants were screened prior to taking part in the intervention for four individual personality domains: (1) hopelessness; (2) anxiety‐sensitivity; (3) sensation seeking; and (4) impulsivity. Students who were classified as being “high risk” on any of the four domains were invited to participate, and assigned to one of four potential intervention workshops. These intervention sessions were CBT‐based and were aimed at each of the four personality domains. Thus, a student who scored highly on the impulsivity measure was assigned to the impulsivity‐focused CBT session. For participants that scored above the mean on multiple measures, they were assigned to the session that corresponded to the personality domain that they deviated the most from standardized scores.

High risk students in each school were randomly assigned to either the intervention or control condition, as were “low risk” students, for comparison. The Preventure study took place between 2005 and 2007, and either a chartered counseling psychology, an experienced special needs teacher, or a master‐level research assistant implemented intervention workshops. In comparison, the Adventure study took place between 2007 and 2009, and although the intervention sessions followed the same procedure, they were implemented by trained teachers in each school.

6.39. *Pro‐ACT+E program

Pro‐ACT+E was a universal, multidimensional program that aimed to prevent bullying in secondary schools (Sprober et al., 2006). It involved a cognitive‐behavioral approach to the problem of bullying and victimization by building up prosocial behavior. The program was universal: it did not involve specific work with perpetrators or victims of bullying. However, it included both teacher and parent training and a 2‐h classroom discussion with students about violence problems. The program offered curriculum materials that aimed to increase awareness in relation to the problem of bullying and placed emphasis on specific issues such as classroom management and classroom rules against bullying.

6.40. *Progetto Pontassieve

The program was delivered in a period of 3 years, and it consisted of two main parts. During the 1st two years it was delivered more at the school level whereas the 3rd year was more at the class and individual level (Ciucci & Smorti,  1998 ). During the 1st year a training course for teachers took place addressing psychosocial risks for children and bully‐victim problems. At the end of the training, a study was conducted to reveal how serious was the problem of bullying and what were its characteristics. The 2nd year of the intervention included a counseling service for each individual who was affected by bullying.

The intervention took place in the 3rd year and was based on the use of two different methods: Quality Circles, where pupils had to cooperate to find practical solutions to their problems, with the use of the Interpersonal Process Recall which consisted of the recording of one Quality Circle and discussion about it. The other method used was Role Playing conducted in small groups with subsequent class discussions, which helped students to examine possible strategies to face and overtake bullying problems. The aims of both of these methods were to make students aware that they could intervene in an efficient way to reduce bullying.

6.41. *Project Ploughshares for Peace

Project Ploughshares Puppets for Peace (P4 program) was an antibullying program that aimed to educate elementary school students about bullying and conflict resolution (Beran & Shapiro,  2005 , p. 703). The P4 program used puppets and a 30‐min script. Using three‐feet, hand‐and‐rod puppets, two puppeteers enacted a story that involved direct and indirect bullying, as well as a successful resolution to this scenario. These behaviors occurred among two female puppets and a male puppet friend.

After watching the play, students were invited to identify the bullying behaviors. During the discussion, four main strategies—presented as “4 Footsteps”—to deal with bullying were suggested to pupils: (1) ignore, (2) say stop, (3) walk away, and (4) get help. The show took approximately 45 min and aimed to increase children's awareness about which behaviors could be categorized as bullying and to show various strategies that children who were bullied and/or who witnessed bullying could use to discourage it (Beran & Shapiro,  2005 , p. 703).

6.42. Rational Emotive Behavioral Education (REBE) and ViSC

Trip et al. ( 2015 ) implemented a dual program consisting of REBE (Trip & Bora,  2010 ) and ViSC social competence (Strohmeier et al.,  2012 ) elements. These components were combined to address both social and emotional factors involved in bullying and positive youth development (PYD). This program approaches bullying from a sociological perspective, including factors on the individual, family, peer, classroom, and school levels (Espelage & Horne,  2008 ; Swearer & Espelage,  2011 ).

ViSC social competence program is a systemic approach to antibullying that targets students, teachers and parents (Strohmeier et al.,  2012 ). Implemented by teachers in the classroom, the program comprises several intervention units that aim to: (1) foster empathy and perspective training, (2) enhance responsibility, and (3) improve students' behavioral responses to bullying (Trip et al.,  2015 , p. 733).

REBE elements employed by Trip et al. ( 2015 ) on the other hand, target specific elements of aggression that are lacking in the ViSC units. Based on the theory of Rational Emotive Behavioral Therapy (Ellis,  1962 ), the REBE elements of the intervention program target the difference between desire and reality (Trip & Bora,  2010 ) and anger. The REBE program activities target specific elements of anger, specifically, anger triggers, personal experiences of anger and the consequences of anger (Trip et al.,  2015 ).

6.43. Restorative Whole‐school Approach (RWsA)

The RWsA (Hopkins,  2004 ; Morrison,  2002 ) was a school‐based antibullying initiative that employs a restorative justice inspired philosophy. Hence, the program focuses on creating a positive school environment to prevent bullying in the long‐term, rather than a short‐term disciplinary and punishment approach (Wong et al.,  2011 ). The program had three core goals: (1) to create a positive and harmonious school learning environment; (2) implement an interactive classroom curriculum; and (3) encourage an effective partnership between teachers, students, parents and relevant professionals.

A whole‐school antibullying nonpunitive ethos and policy is implemented as the core of the intervention (Wong et al.,  2011 ). This policy aims to establish a positive school environment in order to combat bullying‐related risk factors. The curriculum lessons incorporated elements on various issues, including, empathy, assertiveness, coping, problem‐solving, and conflict resolution.

6.44. Resourceful Adolescent Program (RAP)

The RAP is a classroom‐based CBT intervention designed for adolescents aged 12–15 years of age (Stallard et al.,  2013 ). The program is a depression prevention program, however, bullying problems were included as secondary outcomes. The program incorporates a detailed manual and student workbooks, and was implemented over nine sessions, of approximately 50–60 min each. The core components include: psycho‐education, helpful thinking, identifying personal strengths, keeping calm, problem solving, support networks, and keeping the peace. The program was designed to flexible and adaptable to participating schools' varying busy timetables.

6.45. *S.S. Grin

The Social Skills Group Intervention (S.S. GRIN) was a school‐based program that aimed to help children enhance their social skills. S.S. GRIN was designed as a social‐skills training intervention for peer‐rejected, victimized, and socially anxious children. It could be applied to an array of problems that are social in nature (e.g., aggression, low self‐esteem, depression, social anxiety, social withdrawal) not just bullying (DeRosier & Marcus,  2005 , p. 140). The authors argued that the program went beyond the most common social‐skills training (De Rosier & Marcus, 2005, p. 141) by emphasizing the cognitive aspects of relations and emotions. That is, children were not only taught prosocial skills, but they were also taught, on the cognitive level, how to identify negative perceptions and behaviors in an effort to help children to regulate their own emotions as well as enhance their coping skills.

Overall, the program was a combination of social‐learning and cognitive‐behavioral techniques, used to help children build social skills and positive relationships with peers. It was a highly structured, manualized program (DeRosier,  2004 , p. 197) with a number of sessions containing scripts and activities to undertake. Each session included didactic instruction combined with active practice such as role‐playing, modeling and hands‐on activities (De Rosier, 2004, p. 197). The children participated in group sessions for eight consecutive weeks. Each session lasted approximately an hour. The groups were led by each school's counselor and an intern, who were trained and supervised by one of the program instructors (De Rosier & Marcus, 2005, p. 143).

6.46. School‐based Drama program

This school‐based antibullying program was based on drama (Owens & Barber,  1998 ) and social cognitive theories (Bandura,  1978 ). The main aim of this project was to design and implement a drama‐based program to improve social relationships and social/emotional well‐being in children, which in turn may help to reduce bullying (Joronen et al.,  2011 ). Targeted concepts included: empathy; social competence; student‐teacher interaction; child–parent interaction; and recognition of values/emotions.

This program was developed by the combined efforts of researchers, drama experts and teachers. It was implemented in‐class by trained teachers and school nurses over a period of 6 months. Teachers and school nurses attended a 2‐day seminar and received two drama handbooks, however, there was no manual or fixed program outline provided. Support was provided through email communication between teachers and researchers for the duration of program implementation. Teachers conducted one drama session per month with their class. These sessions covered a variety of topics, including, bullying, friendship, loss of a friend, supporting a bullied peer, tolerance, and child abuse.

6.47. School‐wide Positive Behavioral Interventions and Supports (SWPBIS)

SWPBIS was a universal behavioral intervention program that targets school‐level factors in order to improve school climate and promote positive student and staff behaviors (Waasdorp et al.,  2012 ). Instead of following a specific antibullying curriculum, SWPBIS aimed to reduce bullying by targeting schools' discipline and behavioral management strategies. A SWPBIS team in each school organized and facilitated the intervention implementation.

These teams were responsible for developing a set of “positive expectations” for the school. These were a number of statements that outlined what the school expected in relation to student and staff behavior, for example, “be responsible, respectful, and ready to learn” (Waasdorp et al.,  2012 , p. 150). Posters highlighting the expectation statements were then displayed all around participating schools, both in classrooms and outside of classrooms, and are positively reinforced using reward systems. Furthermore, data from student surveys and discipline referrals were employed throughout the intervention to inform teachers of potential bullying “hot spots” that require increased supervision and monitoring. School staff also received training on classroom management and how to respond consistently and effectively to bullying. Additionally, students identified as being “high risk” or vulnerable to bullying behaviors or victimization were provided with selective intensive intervention.

6.48. School bus antibullying intervention

This intervention program was a universal antibullying program designed to reduce the prevalence of bullying behaviors on school buses (Krueger,  2010 ). The program was purposefully developed and utilizes materials and content from the “Take a Stand, Lend a Hand, Stop Bullying Now!” tools that are available free of charge.

The intervention was implemented with elementary school children over five consecutive days, during the final 20‐min of the school day. Lessons were delivered by the school's social worker and principal to two groups (kindergarten to 2nd grade students, and 3rd to 5th grade students) of participants. The program followed this format from days 2–5, however, on day 1, all participants completed the introductory lesson together. The school‐bus antibullying program primarily utilized DVD materials from the “Take a Stand” content. These video clips depicted cartoon characters engaging in different bullying scenarios.

On day 1 (i.e., the introductory lesson) an overview of school bullying and related issues, including bystander intervention, was provided to participants. The associated DVD clip depicted a male character physically bullying another child in the playground while other students watched. Participants then discussed the clip in groups, and were introduced to the “Three Steps to Stop Bullying Chart.” This technique involves three steps, Stop, Help , and Tell , that bystanders can take if they witness bullying.

On each subsequent day, a new DVD clip was shown to participants and the Stop, Help , and Tell concepts were revisited. The school's social worker or principal led discussion groups by posing questions to the students concerning the feelings and emotions experienced by the victim of bullying, potential coping strategies that the victim could use, and possible bystander behaviors. Participants also shared their previous experiences with similar situations. Furthermore, using the Stop, Help , and Tell paradigm, participants brainstormed potential ways to tell a bully to stop behaving in a certain manner, ways to help the victim and appropriate trusted adults that they can tell about the situation.

6.49. Second Step

The Second Step: Student Success Through Prevention is a middle school Social‐Emotional Learning (SEL) program that aims to reduce bullying, peer victimization, physical aggression, homophobic name‐calling and sexual violence (Espelage et al.,  2013 ,  2015 ). The intervention curriculum is taught in‐class by trained teachers. Lessons are interactive and engaging, requiring students to take part in whole‐class, small group and individual work. A take home task is also given after each lesson to reinforce skills learned. DVDs are also used to accompany and enrich lesson content.

The 6th grade Second Step curriculum involves 15 weekly lessons on various social and emotional skills and bullying‐related topics. The following outlines the curriculum: (1) empathy and communication—five lessons; (2) bullying—two lessons; (3) emotion regulation (e.g., coping with stress)—three lessons; (4) problem‐solving—two lessons; and (5) substance abuse prevention—four lessons.

Each lesson has clearly outlined learning objectives to reduce problem behaviors and increase prosocial behaviors. For example, lessons on bullying target the peer context by increasing knowledge, improving attitudes, and encouraging bystander intervention in order to reduce bullying perpetration and victimization. Students are educated about the differences between types of bullying, importance and responsibilities of bystanders in preventing bullying and a number of positive bystander behaviors are modeled. The 7th grade Second Step curriculum involves a similar lesson structure, with some slight changes. The intervention is delivered over 13 weekly lessons, and cyber‐bullying and sexual harassment issues are incorporated into bullying modules.

6.50. Shared Concern

Wurf ( 2012 ) assessed the effectiveness of the whole‐school approach to bullying intervention and prevention, with a particular emphasis on Pikas' ( 2002 ) nonpunitive method of shared concern. The Pikas method of Shared Concern is a teacher, or counselor, implemented intervention, that is divided into five key stages. First, the intervener identifies the students involved in bullying and talks with them individually. These discussions aim to provide nonpunitive and constructive options for both bullies and victims (Wurf,  2012 ). The second and third stages involve providing empathy and ongoing support to the victims of bullying. Finally, the fourth stage incorporates a mediation session between bullies and victim(s). A conflict resolution approach to prevent bullying is agreed upon and implemented by all involved. The fifth and final stage occurs during the follow‐up period, whereby the teacher or counselor monitors the involved students to ensure that the bullying has stopped.

6.51. *Short Intensive Intervention in Czechoslovakia

The antibullying intervention in Czechoslovakia was inspired by the OBPP and borrowed elements from it, such as the Olweus videocassette on bullying (Rican et al.,  1996 , p. 399). The Olweus bullying questionnaire was used to measure several aspects of bullying within the schools. A peer nomination technique was also used to identify bully and victim scores. The relevant results from both measurement scales were presented to teachers in the intervention schools to increase awareness of the problem of bullying. The program researchers discussed with the teachers “possibilities of an individual approach to the bullies as well as to the victims” (Rican et al.,  1996 , p. 399).

As another intervention element, teachers were instructed to introduce relevant ethical aspects into the curriculum where possible: the ideal of knighthood was suggested for history classes and the ideal of consideration for the weak was introduced in sentences used for dictation and analysis (Rican et al.,  1996 , p. 400). Another element of the intervention involved the use of a method called “class charter.” Specifically, children were asked to indicate how they would like their teachers and other classmates to behave toward them as well as how students should behave toward teachers and among themselves. The final aim of this classroom activity was the construction of a set of rules and principles, which was then signed by all pupils in the classroom and placed there in a visible position. Finally, the Olweus video‐cassette on bullying was shown to children and was used as a means of promoting the antibullying idea in the school.

6.52. *Short Video Intervention

This antibullying strategy, involved a single viewing of an antibullying video, entitled Sticks and Stones, and aimed to examine its effects on secondary school students' views of, and involvement in, bullying. The program aimed to examine both attitudes toward bullying and the actual behavior since “it would not be unreasonable to propose that these attitudes will influence actual behavior” (Boulton & Flemington,  1996 , p. 334). The program involved only one school that had no prior antibullying policy.

The video presented pupils (either in groups or on their own) talking about bullying, their views about this phenomenon and their personal experiences of bullying. The video also involved a number of bullying scenes (see Boulton & Flemington,  1996 , p. 337 for examples).

6.53. Social and Emotional Training (SET) intervention

This intervention program was a school‐based SET mental health program for Swedish school children (Kimber et al.,  2008 ). The SET program was primarily focused on mental health, but also targeted other aspects of participants' lives, such as bullying. Both internalizing and externalizing aspects of child mental health are addressed.

Trained teachers delivered the program over the course of two academic years. Intensity of program implementation varied according to the age of students. Junior students (i.e., grades 1–5) received the program in 45‐min sessions twice a week, while senior students (i.e., grades 6–9) completed one 45‐min session per week. Program developers provided each participating teacher with detail manuals for implementing the program with each grade and grade‐specific student workbooks. Role‐playing and modeling tasks covered many themes, including: social problem solving; conflict management; dealing with strong emotions; and resisting peer pressure. Teachers were also supervised once a month during the 1st year of implementation, and students were encouraged to practice skills both at school and at home.

6.54. Social Norms Project

Lishak ( 2011 ) implemented an antibullying program based on social norms theory (Perkins,  2003 ) with middle school students. The program was implemented over a period of 12 weeks and was developed based on student responses to an anonymous web‐based survey and student discipline and suspension reports (Lishak,  2011 ). Student surveys collected information regarding perceptions of bullying in the school and results were then relayed to participants via weekly lessons, assemblies, posters, and media content throughout the school. Data from school discipline, suspension and visitation logs were collated to estimate the prevalence of bullying and school violence.

6.55. *Social Skills Training (STT) program

STT was a program specifically designed to support “chronic victims” of bullying (Fox & Boulton,  2003 , p. 237). The general aim of the program was to help children improve their social skills, therefore reducing a child's individual risk of victimization (Fox & Boulton, 2003 , p. 234). The program involved an 8‐week course during which children learnt how to use both problem‐solving and relaxation skills, how to think positively, how to modify their nonverbal behavior and how to use some verbal strategies such as “fogging” and “mirroring” (Fox & Boulton,  2003 , p. 235).

During the program, victims of bullying were gathered in groups of five to ten and were exposed to the aims of the program for 1 h/week. Two trainers delivered the 1‐h sessions throughout the program. The 1st week was dedicated to children introducing each other and listening each other's problem. The next two sessions dealt with issues of friendship and aimed to help children form strong friendships (e.g., having conversations; asking to join in), while the fourth session dealt with issues of body language: teaching children how to modify their nonverbal behavior in a way that would protect them from being victimized. During the fifth session children learned how to be assertive while in the next two sessions children were taught how to deal with the bully. The eighth session signaled the end of the program.

6.56. *SPC and CAPSLE program

This evaluation compared the effects of two intervention packages with a treatment‐as‐usual condition (Fonagy et al.,  2009 ). Nine schools were randomly allocated to the two experimental and one control (treatment‐as‐usual) conditions after a stratified allocation procedure, which was used to stratify schools based on the percentage of low‐income students (indicated by students' free‐ and reduced‐lunch status). In the experimental conditions, the full intervention was offered for 2 years (the efficacy phase) with a limited 3rd year of intervention (the maintenance phase).

The first experimental condition involved a “School Psychiatric Consultation” (SPC), a manualized protocol that aims to address mental health issues of children with disruptive behavioral problems, internalizing problems, or poor academic performance. SPC was a school‐level intervention focused on individual children. Three child psychiatry residents, supervised biweekly by a senior child psychiatrist, delivered mental health consultation following the SPC manual for 4 h/week. The psychiatric residents attended weekly school resource meetings and consulted directly with teachers, parents and other school personnel, through classroom observations and meetings, providing 140 consultations for 65 students in year 1 and 97 consultations for 45 students in year 2.

The second experimental condition involved the implementation of CAPSLE (“Creating a Peaceful School Learning Environment”), a manualized psychodynamic approach addressing the cocreated relationship between bullies, victims and bystanders. In contrast to SPC, CAPSLE represents a whole‐school intervention approach. It aimed to modify the educational and disciplinary school climate. A CAPSLE team drawn from school staff in the pilot project led implementation in the two intervention years using a training manual. In year 1, teachers received a day of group training, students received nine sessions of self‐defense training, and the CAPSLE team consulted with school staff monthly. Year 2 started with a school‐wide half‐day refresher self‐defense course, and consultation continued with counselors, teachers and adult/peer mentor programs. In year 3 (the maintenance phase), self‐defense training continued as in year 2.

CAPSLE includes several antibullying materials that can be used by teachers such as a Teacher Discipline Manual (used in the teacher training), a Student Workbook, Buttons and Magnets and Patches (used as a way of reinforcing of desirable student behavior), Parent Warning Notes (notifying parents about specific problem behavior of the child) as well as antibullying videos that can be used during the physical education lessons (and videos that can be used by parents). CAPSLE also includes the Gentle Warrior Program, a 12‐week curriculum specifically designed for physical education teachers. For CAPSLE, intervention fidelity was assessed using a teacher self‐report measure that required teachers to state the frequency with which various CAPSLE program components were implemented.

6.57. Standard CBT and CBT plus media program

This intervention program combined elements of standardized CBT and DVD bullying‐related materials in order to reduce bullying perpetration and victimization among elementary school children (McLaughlin,  2009 ). The standardized CBT lessons were delivered by a trained counselor, and focused on bullying and aggression relation issues. Two experimental groups were employed, one of which received only the CBT lessons, and the other completed the CBT lessons and were shown the bullying DVDs.

The program was implemented over 4 weekly lessons that followed a strict outline. In week 1, the lesson focused on defining bullying, identifying bullying roles and different forms of bullying, and exploring the possible characteristics of bullies, victims, and bystanders. Week 2's lesson was concerned with establishing the consequences of bullying for all those involved, including the bully, victim and bystanders. Empathy for victims of bullying was also developed. Activities included creating feeling lists, and participating in role plays. Lesson three aimed to promote bystander intervention by developing awareness and knowledge of appropriate responses to bullying, suitable ways to intervene, and promoting assertiveness. Classes are taught using educational and informative posters. The final lesson, in week 4, aimed to outline the gender differences in bullying, why these occur, and ways to combat gender‐specific forms of bullying. In their classes, students establish class antibullying rules and are taught about the support available in school to stop bullying.

In addition, students in the CBT + media experimental group watched three DVDs that highlighted the issues outlined in the weekly lessons. The DVDs that were shown are as follow: (1) Let's Get Real , which shows young people talking about their personal experiences of bullying; (2) The Deepest Hurt , that depicts girls role‐playing various scenarios of relational aggression; and (3) The Broken Toy , a dramatization of the damage bullying can cause. Following the videos, students engaged in group discussions led by the counselor about the issues illustrated in each DVD.

6.58. *Stare bene a scuola: Progetto di prevensione del bullismo

This intervention was based on the curriculum activities and the whole school approach because it tried to involve all people in a school (Gini et al.,  2003 ). The program was delivered to 6 schools and included several activities. Teachers were first trained in 3 days on “cooperative learning” and in particular on the Jigsaw technique. Teachers then had an on‐going supervision once every 15 days. The intervention in the class lasted 4 months with two meetings a week. The intervention was directed toward the following areas: (1) awareness of the body and what it feels; (2) emotional awareness; and (3) bullying awareness. These areas were dealt with in each of the sessions, starting from the first one. For each thematic area, several activities were conducted and several methods were used.

6.59. Start Strong

“Start Strong: Building Healthy Teen Relationships” was a school‐based curriculum focused teen dating‐violence prevention program (Williams et al.,  2015 ). The program was implemented over 2 years in four experimental schools (that implemented the program) and four comparison schools (that did not implement the program). Schools were matched based on: school size, percentage of students eligible for free school lunches, race/ethnicity, and socioeconomic status. The effectiveness of the program was measured for outcomes that included the perpetration and victimization of teen dating‐violence, bullying and sexual harassment.

6.60. *Steps to Respect

The Step to Respect program aimed to tackle bullying by: (1) increasing staff awareness; (2) fostering socially responsible beliefs; and (3) teaching social‐emotional skills so as to promote healthy relationships (Frey et al.,  2005 , p. 481). The program included staff and family training manuals, a program guide and lesson‐based curricula for third‐ through sixth‐grade classrooms (Hirschstein & Frey,  2007 , p. 7).

Components at a whole school level consisted of an antibullying policy and procedures, staff training and parent meetings, all aiming at sharing understanding of bullying and its consequences and increasing adult awareness, monitoring, and involvement. At the classroom level, the proposed activities consisted of teaching friendship skills, emotion regulation skills, identifying types of bullying, teaching prevention strategies and peer group discussion. The aim was to improve peer relations and reduce the risk of victimization, assess level of safety and recognize, report and refuse bullying. At the individual level, students involved in bullying were approached and coached based on the “Four‐A Responses”: affirm behavior, ask questions, assess immediate safety and act.

The S to R training manual consisted of an instructional session for all school staff and two in‐depth training sessions for counselors, administrators, and teachers. There were also videos accompanying the program. With regard to staff training, there were two levels of training: all school staff received an overview of the program goals and principal aspects of the program (program guide). Teachers, counselors, and administrators received additional training in how to coach students involved in bullying, based on behavioral skills training, cooperative learning and role‐playing.

The student curriculum comprised skills and literature‐based lessons delivered by third‐ through sixth‐grade teachers during a 12–14‐week period. The intervention consisted of 10 semi‐scripted skills lessons with topics such as joining groups, distinguishing reporting from tattling and being a responsible bystander.

Finally, with regard to the parent intervention, administrators informed parents about the program and the school's antibullying policy and procedures. Parents could also benefit from other resources such as letters provided to them and newsletters describing whole‐school antibullying activities undertaken at school.

6.61. Strengths in Motion (SIM)

The SIM (Rawana et al.,  2011 ) program was a strength‐based whole school antibullying intervention. There were several components involved in the program, all of which centered around a strength‐based approach. This technique involves highlighting and enhancing individuals' strengths in order to develop positive mental health (Duckworth et al.,  2005 ). In the context of the present evaluation, Rawana et al. ( 2011 ) requested that each participating school allocated one room as a designated intervention resource room. In the first instance, this room acted as a “Good Start Centre” (p. 287) where new students to the school were provided with two half‐day orientation sessions prior to starting school. Part of these orientation sessions was individualized strength assessments. It was predicted that by providing new students with guidance on how to best use their strengths to integrate successfully into school life the likelihood of future bullying and victimization would be reduced.

The second use of the intervention room was as a “Cool Down & Prevention,” where students experiencing behavioral or emotional problems could go to calm down. Staff were on hand to prevent the behaviors from escalating and offer helpful advice. The room also acted as an alternative to suspension from school, whereby students could be mandated to spend a certain number of days in the “Good Choices Room.” An ambassador's club for students identified as being at high risk for bullying perpetration or victimization was also held in the resource room. Finally, mental health professionals provided student and parent workshops and staff received tailored training on the strength‐based approach to bullying prevention and intervention.

6.62. Take the LEAD (TTL)

The TTL (Domino,  2011 ,  2013 ) program was designed to increase the social competencies of participants in order to reduce bullying behaviors. The intervention is based on SEL and PYD theories.

Various social and emotional skills are targeted during the 16‐weekly lesson curriculum, including: (1) Self‐awareness; (2) Self‐management; (3) Social‐awareness; (4) Relationship skills; (5) Decision making; (6) Problem solving; and (7) Leadership. Trained teachers taught TTL lessons during normal class periods on a weekly basis. Participating teachers were trained on the skill‐based curriculum by the developers of the TTL program. During training, teachers were taught about specific learning objectives and goals of the intervention program, and also about the lesson plans and activities involved in “Take the LEAD.” Information evenings for parent were also held as part of the TTL intervention and aimed to raise parents' awareness of key social‐emotional issues.

Each of the sixteen TTL lessons involved specific learning objectives and goals. Lessons involved a combination of knowledge and skill development and an application component, so that participants were given the opportunity to apply skills in real‐world settings. For example, the “Communication skills” lesson aimed to “explore elements of communication that enhance interpersonal skills and foster positive relationships (Domino,  2013 , p. 432). During this lesson students brainstormed ideas about effective and positive communication techniques and were then required to practice these skills (e.g., eye contact, active listening and showing empathy) in pairs. Finally, participants were required to practice these techniques in an interview with a classmate, and later with a parent.

6.63. *Toronto antibullying program

The Toronto antibullying program was inspired by the OBPP (Pepler et al.,  2004 , p. 125). It was based on the understanding that bullying is a problem that extends far beyond the individual children; it involved the peer group and the teachers, as well as the parents of children (Pepler et al.,  2004 , p. 127). The program included several preventive elements implemented at the school, parent, and classroom levels, as well as additional work with specific students involved in bullying as perpetrators or victims.

The level of implementation of the program varied across the intervention schools. However, in all intervention schools three critical elements were found: staff training, codes of behavior and improved playground supervision. At the school level an emphasis was placed on developing a positive code of behavior among students, engaging teachers, and promoting positive playground interactions. At the parent level, information nights were held during which parents were informed about the problem of bullying in their school. Also, information about the program and its objectives was sent home. At the classroom level, children were involved in developing classroom rules against bullying. Further classroom activities aimed to change students' attitudes and to promote healthy relationships among peers. At the individual level, children involved in bullying as perpetrators or victims received specialized intervention through consultation and though engaging their parents. Follow‐up monitoring of these cases helped school authorities to establish that bullying incidents were terminated or discontinued.

6.64. *Transtheoretical‐based Tailored antibullying program

This antibullying initiative involved “transtheoretical‐based tailored programs that provided individualized and interactive computer interventions to populations of middle and high school students involved in bullying as bullies, victims and/or passive bystanders” (Evers et al.,  2007 , p. 398). The intervention involved only three 30‐min computer sessions during the school year for the students and a 10‐page manual for staff and parents with optional activities. According to the program designers, the transtheoretical model is “a theory of behavior change that applies particular change processes like decision‐making and reinforcement to help individuals progress at particular stages of change” (Evers et al.,  2007 , p. 398).

Intervention materials included the “Build Respect, Stop Bullying” program, which is a multicomponent, internet‐based computer system (Evers et al.,  2007 , p. 402). Students initiated the program by running a multimedia CD which brought them to the program website. Students could use the program by creating a login name based on personal information and a password. Once the students registered for the program, logged in and consented to be involved in the intervention study, they were given instructions on how to proceed. This multi‐media program also included short movies (videos) of students giving testimonials about bullying (Evers et al.,  2007 , p. 403).

Other elements of the program included: (1) a 10‐page family guide, sent to children's homes, which provided brief information about the multi‐media program and its relation to the antibullying initiative; and (2) a 10‐page staff guide, which included general information about bullying and how to support student change, classroom activities and information on how to work with parents. Teachers were not provided with any training.

6.65. Utrecht Healthy Schools

The Utrecht Healthy Schools program was a comprehensive educational program that targeted adolescent health behaviors (Busch et al.,  2013 ). The integrated program aims to improve various different health‐related behaviors exhibited by Dutch secondary school students, such as, nutrition, exercise, sexual health, substance and alcohol use, smoking behaviors, bullying, and excessive use of television, gaming and Internet use. The program was implemented as a whole‐school approach and consisted of five key components.

First, participating schools implemented a “healthy school” policy outlining a zero‐tolerance attitude toward risky or violent behaviors, such as alcohol use, smoking or bullying. Second, the program aimed to create a healthy school environment by offering healthy options in the canteen, removing vending machines, ensuring proper sports facilities, hosting alcohol‐free school parties and implementing a smoke‐free school yard. In the third instance, the program aimed to involve parents in intervention activities by providing parent workshops and/or take‐home activities for students. Finally, curriculum materials focused on personal skill development and the program aimed to incorporate public health services into the intervention program.

6.66. *Viennese Social Competence Training program (ViSC)

The ViSC aimed to provide students “with systematic theoretically‐based guidance in becoming responsible and competent actors in conflict situations” (Atria & Spiel,  2007 ; Yanagida et al.,  2019 ). It was specifically designed for disadvantaged adolescents aged fifteen to nineteen who were considered at risk for future problems (Atria & Spiel,  2007 , p. 179). The theoretical basis of the programs drew its main ideas from social information processing theory and from research that approached the problem of bullying as a group phenomenon (Gollwitzer et al.,  2006 , p. 126).

The ViSC program consisted of thirteen lessons which were divided into three phases: (1) impulses and group dynamics; (2) reflection; and (3) action. The first phase, entitled “impulses and group dynamics,” consisted of six lessons and the main aim was to enhance students' competence in dealing with critical situations by teaching them how to look at social situations from different perspectives using vignette stories, discussions and role‐plays. The second phase, reflection , involved one lesson during which pupils reflected on what had been learned in the first phase of the program.

The last phase, action , consisted of six lessons during which the trainer asked students to define how they wanted to benefit from the remaining lessons. The trainer collected students' individual ideas, evaluated them and—along with the students—put them in practice in alignment with the global goal of the program: enhancing pupils' social competence. The third phase of the program was flexible and it could involve several projects suggested by pupils such as a movie production, a work of art, the organization of a party, and so on. This flexibility was allowed and was, in fact, a main feature of ViSC because organizing such projects “involves a variety of critical situations, in which alternative, nonaggressive response options can be probed, rehearsed, and evaluated for success” (Gollwitzer et al.,  2006 , p. 126).

Based on the design of the program, the training of students was conducted by specialist trainers, not their teachers. The trainers participated in instruction workshops and were also supervised during the training by the ViSC developers' team at the University of Vienna (Gollwitzer et al.,  2006 , p. 127). According to the principles of the program, it was essential for the trainer to avoid receiving any information about individual students offered by teachers; students' assessments should be based on standardized diagnostic measures (Atria & Spiel,  2007 , p. 184). Moreover, the training was conducted during regular class time and teachers were advised to attend the lessons, so that the program was taken seriously by the students. ViSC has been implemented and evaluated three times: by Gollwitzer (2005), by Atria and Spiel ( 2007 ) and by Gollwitzer et al. ( 2006 ).

6.67. Youth‐led program

The Youth‐led program (YLP; Connolly et al.,  2015 ) was a generalized middle school violence prevention program. This program was developed by a community agency, and involved training high school students to lead violence prevention workshops with middle school students in order to increase the latter's knowledge and attitudes of peer aggression and victimization.

Experienced mental health professionals were employed to select and supervise male and female high school students that would become “youth leaders.” These students received training in afterschool sessions on skills and knowledge of peer aggression. Topics covered included bullying perpetration and victimization, but also peer aggression, violence, and harassment.

The final sessions of this training required the youth leaders to create two individualized presentations; one covering bullying and the other discussing general aggression. Mixed gender pairs of youth leaders then conducted these presentations in middle school classrooms under the supervision of a mental health worker. These presentations lasted for approximately 45 min each.

6.68. *Youth Matters

The Youth Matters program used “a curricular and a modified systemic approach to bullying prevention” (Jenson & Dieterich,  2007 , p. 287). The aim of the curriculum was to strengthen peer and school norms against antisocial behaviors by addressing critical issues (issue modules) such as the difference between teasing and bullying, building empathy, risks and norms surrounding aggression and so on. The curriculum also aimed to promote skills (skill modules; structured skills training sessions) that students could use in order to stay safe at school, cope with bullying, enhance their social skills and improve their peer relationships. To address systemic issues associated with bullying, curriculum modules terminated with the development of classroom or school‐wide projects, which placed emphasis on the negative consequences of bullying for students.

The curriculum consisted of 10‐session modules. Each module included a 30–40‐page story, the content of which was directly linked to the structured skills training sessions. When looking at the implementation of the program, all curriculum materials were “language sensitive”: translated into Spanish for use in the three Spanish‐speaking classrooms included in the evaluation. Youth Matters curriculum modules were offered to fourth and fifth graders. According to Jenson and Dieterich ( 2007 , p. 287), grades 4 and 5 were selected “based on an appropriate fit between developmental ability and curricula.”

The Youth Matters program was based on a theoretically grounded curriculum. The curriculum was based on theoretical constructs derived from the Social Development Model. The latter integrated perspectives from three theories (i.e., social control theory, social learning theory and differential association theory) and proposed that four factors inhibit the development of antisocial development in children. These were: (1) bonding or attachment to family, schools and positive peers; (2) belief in the shared values or norms of the above‐mentioned social units; (3) external constraints or consistent standards against antisocial behavior; and (4) social, cognitive and emotional skills that can be seen as protective tools for children to solve problems and perform adequately in social situations. The Youth Matters curriculum addressed each of these four core areas.

6.69. Zero program

The Zero antibullying program is based on the idea that bullying is predominately a version of proactive aggression (Roland et al.,  2010 ). The program aims to create a school environment that prevents these forms of proactive aggression. The intervention places the majority of responsibility for bullying prevention and intervention with the adults within the school environment (Roland et al.,  2010 ). School staff were required to define clear standards of positive prosocial behavior among the students and to ensure that these standards are met. Thus, the adults within the school context adhere to a “zero tolerance” policy toward bullying. Another key feature of the intervention is that students are instructed to treat all school property appropriately and respectfully and the intervention philosophy is carried into classroom activities and standards also.

During the intervention, class teachers engage their respective classes in active discussions about issues relating to bullying in adherence with the intervention guidelines. The preventative function of the Zero program takes both a direct and indirect approach (Roland & Galloway,  2004 ). Teachers are also expected to be vigilant and visible in school corridors and playgrounds during nonclass time and follow intervention procedures when dealing with specific instances of bullying (Roland et al.,  2010 ). When particular instances of bullying are identified, the victim is first approached and takes part in a few sessions with trained staff being comforted and assured. Parental involvement also occurs at this point. Finally, the perpetrators are invited to attend meetings and conflict resolution occurs under a restorative justice model.

6.70. Zippy's Friends

Zippy's Friends is a universal school‐based program for children aged 6–8 years old (Holen et al.,  2013 ; Mishara & Ystgaard,  2006 ). The overarching aim of the program is to develop and improve participants' coping strategies in order to reduce and prevent psychological problems. Zippy' Friends has been funded by the global suicide prevention organization “Befrienders International,” and is now distributed internationally by the nonprofit group “Partnership for Children.”

The intervention is delivered over the course of 24 weekly lessons, that are implemented by classroom teachers. The program is based around six stories of the imaginary character “Zippy,” three children, and their families and friends. A structured curriculum outline for each lesson allows participants to engage and discuss the various themes that emerge in each of the stories. Themes that are incorporated include: emotions; communication; friendships; conflict resolution; loss and change.

Teachers are provided with a detailed manual for the program and are required to guide their classrooms through the intervention while also encouraging active engagement with the content. Typical activities that are involved in the Zippy's friends program include: drawing, role‐playing, performing exercises, play and dialogue.

7. RESULTS OF SYSTEMATIC REVIEW

In addition to the newly identified studies ( n  = 88), primary evaluations ( n  = 53) discovered by Farrington and Ttofi ( 2009 ) are also included in the present systematic review, giving a total of 141 studies. However, this updated systematic review has excluded evaluations that used an “other” experimental‐control design ( n  = 13). Next, a detailed explanation is provided about studies which were excluded from the current review and justifications for this decision.

7.1. Studies excluded because of missing information

A certain amount of statistical information is needed in order to produce meaningful effect sizes in a meta‐analysis. We estimated an antibullying program's effectiveness as the difference between the experimental and control groups on bullying outcomes, either measured as the percentage of bullies/nonbullies or victims/nonvictims or based on mean scores on measurement instruments before and after implementation of the intervention.

However, 21 studies identified by our systematic review did not present sufficient effect size information, and so the primary authors of these publications were contacted. We were able to obtain relevant information for the majority of these studies, but three authors were unable to provide required statistics and seven did not respond to our email communication.

Thus, 10 studies had to be excluded from our meta‐analysis because of a lack of information regarding quantitative outcomes. These relate to: Gradinger et al. ( 2015 ); Harpin ( 2011 ); Kyriakides et al. ( 2014 ); Lewis et al. ( 2013 ); Lishak ( 2011 ); Low and Van Ryzin ( 2014 ); van der Ploeg et al. ( 2016 ); Sahin (2012); Schroeder et al. ( 2012 ); and Wurf (2010). In the previous review by Farrington and Ttofi ( 2009 ), 44 out of 53 evaluations provided sufficient information on quantitative outcomes.

7.2. Studies excluded because of nonindependent samples

One further stipulation of a meta‐analysis is that the final samples must be independent of one another (Borenstein et al.,  2009 ; Ellis,  2010 ). Overlapping samples are statistically dependent, and thus the variance of the summary effect size produced by the meta‐analysis would be under‐estimated (Wilson,  2010 ). Therefore, before conducting our meta‐analysis we ensured that all samples were independent of one another.

This issue of nonindependent samples was particularly relevant for the multiple evaluations of the KiVa antibullying program. Our thorough systematic searches identified 16 potentially includable studies presenting evaluation data from implementation of the KiVa program (i.e., Ahtola et al.,  2012 ,  2013 ; Garandeau, Lee, et al.,  2014 , Garandeau, Poskiparta, et al.,  2014 ; Haataja et al.,  2014 ; Hutchings & Clarkson,  2015 ; Kärnä et al.,  2011a ,  2011b ,  2013 ; Nocentini & Menesini,  2015 ; Noland,  2011 ; Sainio et al.,  2012 ; Salmivalli et al.,  2012 ; Williford et al.,  2012 ,  2013 ; Yang & Salmivalli,  2015 ). For a description of each of these studies, see Table  7 .

Description of KiVa studies

* Included in meta‐analysis.

However, following further screening, only four of the aforementioned studies were subsequently included in the systematic and meta‐analytic review (i.e., Kärnä et al.,  2011a ,  2011b ,  2013 ; Nocentini & Menesini, 2016). These four studies presented independent results of the KiVa program from the initial nationwide evaluation in Finland. Kärnä et al. ( 2011a ) used an age cohort design with adjacent cohorts and reported the initial results from the nationwide implementation in Finland. Second, Kärnä et al. ( 2011b ) reported the results from the RCT with Finnish students in grades 4–6, and Kärnä et al. ( 2013 ) reported results for students in grades 1–3 and 7–9. In addition, Nocentini and Menesini (2016) reported the results of the implementation and evaluation of KiVa in Italian schools. The remaining 12 publications relating to the KiVa program utilized data from the RCT evaluation in Finland (i.e., Kärnä et al.,  2013 or Kärnä et al.,  2011b ) but explored different facets of the program's effectiveness.

Four studies identified in our systematic searches replaced evaluations included in the earlier review. For example: (1) Menard and Grotpeter ( 2014 ) was a continuation of the Menard et al. ( 2008 ) evaluation; (2) Cross et al. ( 2011 ) was a republication of the Cross et al. ( 2004 ) evaluation included in the previous review; (3) Jenson et al. ( 2013 ) and Jenson et al. ( 2010 ) presented data from additional follow‐up points to the Jenson et al. ( 2007 ) evaluation; and (4) Frey et al. ( 2009 ) used an age cohort design to evaluate follow‐up effects from the earlier Frey et al. ( 2005 ) study. In cases such as these, the most recent publication, or the publication with the most statistical information, was included in the meta‐analysis.

Ten studies (published both before and since 2009) were identified as reporting the effectiveness of an antibullying program from the same sample, or were repeat publications of earlier studies (e.g., DeRosier,  2004 and DeRosier & Marcus,  2005 ; Domino,  2011 and Domino,  2013 ; Espelage et al.,  2013 and Espelage et al.,  2015 ; Jenson et al.,  2013 and Jenson et al.,  2010 ; and Menesini et al.,  2012 ; Study 2 and Palladino et al.,  2012 ). In these instances, the most recent publications were selected, and as a result, five studies were excluded from the meta‐analysis.

7.3. Included studies

Therefore, 128 studies are included. Table  5 summarizes the intervention programs and methodological components of the 79 newly identified studies that are included in the present systematic review. For details of the remaining 49 studies please refer to Farrington and Ttofi ( 2009 ).

7.4. Moderator analysis

The following moderators were selected a priori for further analysis, under the descriptive label (i.e., location of intervention, publication type, publication year), design label (i.e., evaluation method and unit of allocation/randomization), and the program heading (i.e., name of intervention, COI, and program specificity). Results of these moderator analyses analogous to the analysis of variance (ANOVA) are presented in Sections 8.5.1 to 8.5.7 of the present report.

7.4.1. Evaluation method

The primary moderator chosen for further analysis was evaluation method. Specifically, whether the evaluation was conducted using a RCT, quasi‐experimental with before and after measures (BA/EC) or age cohort (AC) design.

Overall, in relation to bullying perpetration outcomes, 36 evaluations used RCT designs, 31 used BA/EC designs and 14 used age cohort designs. However, due to some evaluations reporting data for multiple independent samples, a total of 40 effect sizes were estimated for bullying perpetration outcomes from RCT designs. A further 36 were estimated from BA/EC designs and 14 effect sizes came from evaluations using age cohort designs.

For bullying victimization outcomes, overall, 33 evaluations used RCT designs that gave 37 independent effect sizes for bullying victimization and 37 evaluations used BA/EC designs and gave 42 independent effect sizes. Similar to perpetration outcomes, 14 evaluations used age cohort designs to evaluate the effect of antibullying programs on bullying victimization outcomes.

7.4.2. Location of intervention

Evaluations included in the present analysis were conducted in many different countries around the world. However, there were only a few countries in which multiple evaluations of antibullying programs had been published.

Specifically, in the following countries only one evaluation was included in the present report: Austria (i.e., Yanagida et al.,  2019 ); Brazil (i.e., Silva et al.,  2016 ); China (i.e., Ju et al., 2009); Czechoslovakia (modern day Czech Republic and Solvakia; i.e., Rican et al.,  1996 ); Hong Kong (i.e., Wong et al.,  2011 ); Ireland (O'Moore and Milton,  2004 ); Malaysia (i.e., Yaakub et al.,  2010 ); Romania (i.e., Trip et al.,  2015 ); Sweden (i.e., Kimber et al.,  2008 ); Switzerland (Alsaker & Valkanover,  2001 ); South Africa (Meyer & Lesch,  2000 ); and Zambia (Kaljee et al.,  2017 ).

If these evaluations were to be included in further moderator analysis, we would be examining the differences based on only one sample and effect size. Therefore, moderator analysis was conducted only between locations in which multiple evaluations of antibullying programs had been conducted.

So, of the 100 evaluations included in our meta‐analysis of school‐based antibullying programs, the majority (80 for perpetration, 84 for victimization) were conducted in one of 12 different countries. With respect to bullying perpetration outcomes, these countries were as follows: Australia ( n  = 2); Canada ( n  = 6); Cyprus ( n  = 3); Finland ( n  = 6); Germany ( n  = 5); Greece ( n  = 2); Italy ( n  = 11); Netherlands ( n  = 3); Norway ( n  = 8); Spain ( n  = 3); UK ( n  = 4); and United States ( n  = 26). With respect to bullying victimization outcomes, these countries were as follows: Australia ( n  = 3); Canada ( n  = 7); Cyprus ( n  = 3); Finland ( n  = 6); Germany ( n  = 4); Greece ( n  = 2); Italy ( n  = 10); the Netherlands ( n  = 3); Norway ( n  = 7); Spain ( n  = 3); UK ( n  = 6); and United States ( n  = 28).

7.4.3. Publication type and year

Overall, the majority of evaluations were published in peer‐reviewed journal articles, for both bullying perpetration ( n  = 67) and bullying victimization ( n  = 72) outcomes. Two evaluations were published in chapters of edited books and both reported effects of a program on both bullying victimization and perpetration. No evaluations identified were published as entire books. Moreover, 12 unpublished dissertations were identified that published evaluation data for bullying perpetration and bullying victimization outcomes. Data was also retrieved for both outcomes from three governmental reports. Four of the effect sizes included in the present report were estimated from data emailed to authors (M. M. T. and D. P. F.) in preparation of the previous Campbell report (i.e., Farrington & Ttofi,  2009 ).

We also categorized included evaluations according to whether they were included in the previous report (i.e., “2009” studies), or only included in the present report (i.e., “2016” studies). In relation to bullying perpetration outcomes, 37 studies were coded as 2009 studies and 53 studies were coded as 2016 studies. Similarly, more studies were coded as 2016 ( n  = 54) studies in comparison to 2009 ( n  = 39) studies for bullying victimization outcomes.

7.4.4. Intervention program

We found that very few specific antibullying programs had been implemented and evaluated more than once using independent samples. Sixty‐five different school‐based bullying intervention and prevention programs were included in our meta‐analysis, but only eight were repeatedly evaluated. Moderator analysis with respect to the specific intervention program therefore, focused on programs that had been repeatedly evaluated.

In relation to reducing bullying perpetration outcomes the intervention programs thus included in our moderator analysis were: BPYS ( n  = 3; e.g., Menard & Grotpeter,  2014 ); fairplayer.manual ( n  = 2; e.g., Bull et al.,  2009 ); KiVa ( n  = 6; Kärnä et al.,  2011b ); NoTrap! ( n  = 4; e.g., Menesini et al.,  2012 ); Second Step ( n  = 3; e.g., Espelage et al.,  2015 ); Steps to Respect ( n  = 2; e.g., Frey et al.,  2005 ); ViSC ( n  = 5; e.g., Yanagida et al.,  2019 ).

Similarly, these interventions were included in our moderator analysis in relation to bullying victimization outcomes with the exception of the fairplayer.manual program. This intervention was evaluated twice only in relation to bullying perpetration outcomes.

Additionally, multiple evaluations of the OBPP were included in our meta‐analysis. Overall, 12 independent evaluations of this intervention were included in our analysis in relation to bullying perpetration and victimization outcomes. These are included in our moderator analysis as a collective subgroup and also as further subgroups. Evaluations of the OBPP conducted in the United States (perpetration n  = 6; victimization n  = 7) and those conducted in Norway (perpetration n  = 5; victimization n  = 5) were included in the moderator analysis separately. There was one evaluation of the OBPP conducted in Malaysia is included in the overall category ( n  = 12).

7.4.5. Unit of allocation/randomization

Systematic review findings showed that one consistent issue with included intervention programs was that the unit of allocation of participants, or clusters of participants, was different to the unit of analysis in most evaluations. Age cohort designs were omitted from this moderator analysis as the unit of allocation was largely unclear due to the logistics of this experimental design.

The majority of RCT and BA/EC evaluations assigned schools to experimental conditions (perpetration n  = 44; victimization n  = 47) yet the unit of analysis was individual students. A number of evaluations (perpetration n  = 19; victimization n  = 15) assigned classes to experimental conditions yet the unit of analysis was individual students. Less than 10 evaluations (perpetration n  = 7; victimization n  = 9) included assigned students to experimental and control conditions. One study randomly assigned districts to experimental conditions, and information was not available for five studies in relation to bullying perpetration outcomes and four studies in relation to bullying victimization.

7.4.6. Conflict of interest

In the present report, 40 studies were categorized as high COI. A large number of studies (perpetration n  = 36; victimization n  = 39) were considered low COI, and 14 were categorized as possible COI. Information concerning COI was unavailable for 4 evaluations in relation to bullying perpetration outcomes.

7.4.7. Program specificity

Overall, a small number ( n  = 11) of studies included in our analysis were coded as low on the program specificity variable. The vast majority of evaluations were considered highly specific (i.e., were mostly concerned with only bullying behavioral outcomes; n  = 59). Additionally, 18 studies were categorized as medium in relation to specificity, where extra outcome variables were measured but these variables were related to bullying (e.g., school climate).

7.5. Risk of bias analysis

Figure  2 presents the results of the risk of bias analysis for each of the items on the EPOC tool and the additional items we included. The following section describes each of these categories in more detail, with examples of high‐ and low‐risk studies included. The main limitation in assessing risk of bias was the lack of information reported by primary studies. Thus, while the best effort was made to categorize each primary evaluation as being high or low risk, a large number of studies were recorded as “unclear” risk.

Risk of bias analysis results. AC, allocation concealment; AS, allocation sequence; BC, baseline equivalence on participant characteristics; BE, baseline equivalence on outcomes; BOA, blind outcome assessment; COI, conflict of interest; CP, contamination protection; ID, incomplete outcome data; SOR, selected outcome reporting

As seen in Figure  2 , the fewest studies were considered unclear risk on CP and selected outcome reporting. Furthermore, a large number of studies were considered low risk on these items.

For the purpose of analysis, the categories high, unclear, and low risk were transformed into scores of 3, 2, and 0 respectively. A continuous “risk of bias” variable was then estimated as the sum total of scores on each of the EPOC items. As such, the lowest possible score a study could be given was zero and the maximum score was 24.

Descriptive statistical analysis identified that risk of bias scores ranged from 0 to 17, with a mean score of 9.62. Meta‐regression analysis was conducted to assess the relationship between risk of bias and effect sizes. The result of this analysis is included in Section  7 of this report. The following sections provide more detail about each of the risk categories.

7.5.1. Allocation sequence

AS refers to the way in which participants, or clusters of participants, were assigned to experimental conditions. For example, low‐risk studies were those where a random number generator or another randomization software was used. In total, 30 studies were categorized as high risk on the AS item. Moreover, 29 studies were low risk and 32 were unclear risk.

7.5.2. Allocation concealment

AC item refers to whether the method of allocation was concealed from participants or not. In total, 36 studies were categorized as high risk on the AC item. A further 19 studies were considered low risk, and 34 were unclear risk.

7.5.3. Baseline equivalence: Outcome

Baseline equivalence refers to the comparability of experimental and control participants before the intervention has taken place. This item specifically refers to equivalence on relevant outcomes, in this case, school bullying perpetration and victimization. When experimental and control participants are not statistically significant at baseline then we can be more certain that any changes are a result of the intervention. Overall, 14 studies were categorized as high risk on the baseline equivalence on bullying outcomes item. A total of 54 studies were low risk and 21 were unclear risk.

7.5.4. Baseline equivalence: Characteristics

Similarly, baseline equivalence on participant characteristics increases the chance that any change is a result of the intervention, and not a confounding variable such as differential participant characteristics at baseline. Overall, 15 studies were categorized as high risk on the baseline equivalence in participant characteristics item, 64 studies were low risk, and 11 were unclear risk.

7.5.5. Incomplete outcome data

Included evaluations were required to incorporate pre‐ and post‐intervention measures of bullying (except if randomization was used). However, because of this, it is likely that there will be some attrition in primary studies. The incomplete outcome data item referred to the risk associated with differential attrition between experimental groups and/or ways in which attrition and missing cases were dealt with by primary studies. Twelve studies were categorized as high risk on the incomplete outcome data item. Additionally, 48 studies were low risk and 29 were unclear risk.

7.5.6. Blind outcome assessment

This item assesses the risk associated with any bias which may arise if outcome measurements are not conducted blindly. In other words, if the individual, or individuals, who administer and collect the measurement instruments are aware of the experimental conditions of participants at the time of measurement. Overall, 27 studies were categorized as high risk on the BOA item. Twenty studies were low risk and 43 were unclear risk.

7.5.7. Contamination protection

Risk of contamination occurs when there is a possibility that experimental and control participants may interact or encounter one another during the course of the evaluation. Thus, the effects of the intervention may “spill over” to control students and impact the results of the evaluation. In our analysis, 35 studies were categorized as high risk on the CP item, 47 studies were low risk, and 9 were unclear risk.

7.5.8. Selective outcome reporting

SOR occurs when the outcomes reported in an evaluation study differ from the outcomes of interest proposed originally. For example, if a trial protocol proposed different outcomes than those actually reported in the publication of the trial results. Two studies were categorized as high risk on the SOR item. Eighty‐four studies were low risk, and three were unclear risk.

8. META‐ANALYSIS

After accounting for missing information, studies excluded because of their methodology (i.e., “other experimental‐control” designs), and studies with overlapping samples, a total of 41 studies were excluded from the meta‐analysis. Thus, a total of 100 studies were eligible for inclusion in our meta‐analysis. Table  8 outlines the raw data from these studies used to estimate effect sizes. The Comprehensive Meta‐Analysis (CMA) software was used to estimate all summary effect sizes in the present meta‐analysis.

Raw data from included evaluations

Abbreviations: A, after; B, before; C, control; E, experimental; M , mean; N , sample size; n , group sample size.

8.1. Effect sizes

A meta‐analysis aims to estimate comparable effect sizes from multiple primary studies. The choice of effect size depends on how statistical information is reported by primary studies (Borenstein et al.,  2009 ). In meta‐analyses such as this one, the data is largely presented in continuous (e.g., means, standard deviations, sample sizes) or dichotomous (e.g., prevalence or percentages) forms (Wilson,  2010 ). Thus, primary effect sizes estimated were Cohen's d and Odds Ratios.

As previously mentioned, we aimed to estimate one effect size for each independent sample included in primary studies. Therefore, where studies reported results separately for male and female participants, or primary and secondary school students, one effect size was calculated for each group.

For primary studies that presented results as percentages or frequencies of participants identifying as either bullies or victims, the odds ratio (OR) effect size was estimated. The ORs for before and after intervention time‐points were calculated independently. The CMA™ software that we used to analyze effect sizes in the present report did not allow us to enter raw data for before and after time‐points for primary studies that reported dichotomous outcomes separately. Thus, we were unable to use this software to calculate a pre‐post intervention estimate for these studies. Hence, these calculations were carried out manually, 5 by the first author, using the method outlined by Farrington and Ttofi ( 2009 ).

Cohen's d was estimated for primary studies when results were reported in the form of continuous data. Cohen's d is estimated as the difference between experimental and control means divided by the pooled standard deviation (Wilson,  2010 , p. 184). Effects were assigned a positive direction in cases where bullying was less in the experimental group compared to the control group or where the reduction in bullying outcomes was larger in the experimental group in comparison to the change in the control group. Following this logic, a negative effect was found when there was: (1) a larger reduction in the control group compared to the experimental group; or (2) there was no change or increase in bullying perpetration/victimization in the experimental group but a reduction or smaller increase in the control group.

For comparability, all effect sizes were converted to ODs. Summary mean effects for bullying perpetration, bullying victimization, and for each of the moderator subgroup are thus reported as odds ratios. In the present review, odds ratios greater than one represent a positive, or desirable, intervention effect. Namely, a reduction of bullying in the experimental group, that is comparably larger than the change in bullying in the control group. Therefore, the change is attributed to have occurred because of the intervention program. Similarly, odds ratios less than one represent a negative, or undesirable, intervention effect and odds ratios that equal one represents a null effect.

8.2. Corrections for clustering

As the present review aims to evaluate the effectiveness of school‐based antibullying programs, cluster‐randomized trials were included. Clustering is a common phenomenon in educational evaluations (Donner & Klar,  2002 ), and occurs when “clusters,” not individuals, are randomly assigned to experimental conditions (Higgins et al.,  2011 ). In other words, primary studies sometimes assigned classes or schools to intervention and control conditions, rather than individual students.

Often this approach is utilized in evaluation studies to reduce treatment contamination and increase administrative convenience (Donner et al.,  2001 ). However, one of the main issues with incorporating cluster‐randomized trials in a meta‐analysis is that participants within a cluster are likely to be more homogeneous than participants in another cluster (Higgins et al.,  2011 ). Thus, the variance of estimates of treatment effectiveness will be under‐estimated (Donner & Klar,  2002 , p. 2974). Clustering could occur for several reasons in studies included in the present report. For example: (1) classes of children, not individual children, were e randomized to intervention or control condition; (2) the intervention was implemented at the classroom level (i.e., to a class or group of children at one time); or (3) the intervention was targeted at teachers, who were trained to implement the intervention in their respective classrooms.

Therefore, effect sizes in the present meta‐analysis were corrected for the inclusion of clusters in primary studies. This is achieved by estimating a design effect:

where M represents the mean cluster size in each study (e.g., the mean number of students per classroom 6 ) and the ICC is the intraclass correlation coefficient.

The ICC is rarely reported by primary studies (Higgins et al.,  2011 ; Valdebenito et al.,  2018 ). Based on Murray and Blitse ( 2003 ), and subsequently the strategy followed by Farrington and Ttofi ( 2009 ), an ICC of 0.025 was assumed in the current meta‐analysis. The variances of effect sizes were then multiplied by this design effect estimated for each study. In the present meta‐analysis, there were only four studies where corrections for clustering were not required. Three studies (i.e., Berry & Hunt,  2009 ; Knowler & Frederickson,  2013 ; Meyer & Lesch,  2000 ) randomly assigned participants to experimental conditions, and Elledge et al. ( 2010 ) described an intervention that was not implemented in a classroom (i.e., the intervention occurred in one‐on‐one sessions with victims of bullying).

8.3. Computational models

The results of our meta‐analysis are presented using two different models. First, we will report the results as estimated using a random effects model that weights studies, largely in proportion to the between‐study variance and accounting for sampling error, thus allowing for the natural variation that occurs between primary studies (Borenstein et al.,  2009 ). We also present the results under the MVA model (Jones, 2005; Farrington & Welsh,  2013 ). which uses the same estimation of a mean effect size as the fixed effects model in that it assigns greater weight to larger evaluations, but also accounts for the between‐study heterogeneity. The MVA model takes account of the heterogeneity of effect sizes to fit the data exactly and yields the same mean effect size as a fixed effect model, but with and increased confidence interval. 7

Farrington and Welsh ( 2013 ) have argued that larger evaluations should be given more weight, and that adding to the variance of effect sizes in order to reduce the heterogeneity is not an optimal method of estimating the weighted mean effect size. When there is considerable heterogeneity in effect sizes, all studies tend to be given much the same weighting in a random effects model. Therefore, several effect sizes from independent samples in one study (e.g., a multisite evaluation) will have a greater weight in the random effects model than in the fixed effects model.

Comparing six models of estimating mean effect sizes for the impact on CCTV on crime rate, Farrington and Welsh ( 2013 ) found that five of the six models produced very similar mean odds ratio effect sizes, with the exception of the random effects model. In this case the random effects model estimated a much higher mean odds ratio (Farrington & Welsh,  2013 , p. 11).

The MVA model is suggested as an alternative approach that overcomes the issues of the random effects model. This technique can be seen as an adjustment to the fixed effects model and combines both the strengths of the fixed effects model (i.e., larger studies = larger weights) and the random effects model (i.e., adjusting for highly probable between‐study variance), and has been used in several meta‐analyses from both the behavioral sciences (e.g., Portnoy & Farrington,  2015 ; Ttofi et al.,  2016 ; Zych, Baldry, et al.,  2019 ; Zych, Viejo, et al.,  2019 ) and medical sciences, where this is known as the “Shore adjustment” (e.g., Ayieko et al.,  2014 ; Carlos‐Wallace et al.,  2016 ; Erren et al.,  2009 ; Steinmaus et al.,  2008 ).

A full review of the strengths and limitations of this model is beyond the scope of the current review. Therefore, in our current meta‐analysis we report mean effect sizes for the impact of antibullying programs on bullying perpetration and bullying victimization using both the random effects model and the MVA model. In later sections, we discuss the differences in the weighted mean effect sizes according to the model chosen.

8.4. Moderator analysis

In traditional empirical research when one wishes to compare two mean values to evaluate the difference between two participants, or two groups of participants, a t test is the standard statistical test. In meta‐analysis, we want to compare subgroups of studies rather than sub‐groups of individuals, so the analysis is slightly different. We followed guidelines provided by noted meta‐analysts for this type of analysis (Borenstein et al.,  2009 ; Lipsey & Wilson,  2001 ).

Our approach involved two steps: (1) computing the mean effect and variance for each subgroup; and (2) comparing the mean effects between subgroups (Borenstein et al.,  2009 , p. 152). This approach has been used previously by researchers to conduct similar analyses (e.g., Kaminski et al.,  2008 ; Ttofi & Farrington,  2011 ).

Comparing the mean effect sizes for subgroups involves a method that is analogous to a one‐way ANOVA in primary research (Hedges,  1982 ; Lipsey & Wilson,  2001 ; Wilson, 2002). The meta‐analyst creates mutually exclusive categories of primary studies and then compares the between‐studies ( Q B ) and the within‐studies ( Q W ) variance.

The between‐studies heterogeneity is the value used to evaluate whether the difference between subgroups is statistically significant (i.e., whether the difference in weighted mean effect sizes for subgroups is, at least partially, explained by the relevant intervention component). Similar to a one‐way analysis of variance, this approach partitions the variance and compares the variability between‐groups. The following formula is used to estimate the Q B :

The degrees of freedom for the between‐studies heterogeneity is estimated as j  − 1 and the statistical significance is determined using a χ 2 distribution. As Q B is estimated using the weights assigned to observed effect sizes, the value will vary between the fixed effects model and the random effects model. Q B is not reported for comparisons of subgroups with very unequal numbers of studies (e.g., location of the evaluation). Under the MVA model, the heterogeneity between groups is estimated by dividing the fixed effects Q B by Q/df . The present report presents results from moderator analysis under both the random effects and MVA models.

8.5. Meta‐regression analysis

CMA™ version 3 software was used to conduct meta‐regression analysis to explore the relationship between continuous moderator variables and perpetration and victimization outcomes. Weighted regression analysis (Lipsey & Wilson,  2001 ) were used to explore which moderators were independently related to school bullying perpetration and victimization. Meta‐regression analyses were only conducted for continuous moderator variables.

Meta‐regression analyses were computed under a fixed effects model, and the standard error of regression coefficients were adjusted using the MVA model. The Q and df of Q for the mean summary effect sizes for subgroups were used to adjust the standard error to reflect between‐study variance.

9. RESULTS OF META‐ANALYSIS

In total, 100 studies were included in our meta‐analysis of the effectiveness of school‐based antibullying programs. From these evaluations, we were able to estimate 103 independent effect sizes. These are presented for bullying perpetration and bullying victimization outcomes in Tables  8 and  9 , respectively. The majority of these effect sizes were estimated from studies that used RCT designs ( n  = 45 effect sizes) or BA/EC designs ( n  = 44 effect sizes). We estimated the remaining 14 effect sizes from age cohort designs.

Meta‐analysis results: School‐bullying perpetration outcomes

Abbreviations: BA/EC, before‐after/experimental control designs; CI, confidence intervals; MVA, multiplicative variance adjustment; OR, odds ratio; RCT, randomized controlled trial; Sig, statistically significant.

9.1. School‐bullying perpetration outcomes

Overall, we found that antibullying programs significantly reduced bullying perpetration under both computational models of meta‐analysis. The effect sizes for each evaluation are presented in Table  9 . The mean summary effect sizes were similar under both the multivariance adjustment model (MVA: OR = 1.324; 95% CI 1.27–1.38; z  = 13.4; p  < .001; I 2  = 81.42) and the random effects model (RE: OR = 1.309; 95% CI: 1.24–1.38; z  = 9.88; p  < 0.001; τ 2  = 0.044).

This result indicates that participants in primary studies who received an antibullying intervention were less likely to report engaging in bullying others after completing the program in comparison to control students who did not partake in the program.

Analysis of the funnel plot (Figure  3 ) suggests that publication bias is not present, as studies are symmetrically distributed around the mean effect size. In addition, point estimates did not vary using Duval and Tweedie's trim and fill procedure under a random effects model (in both cases: OR = 1.308; 95% CI 1.240–1.380). Based on these results, it was reasonable to assume that publication bias was not likely.

Publication bias analysis: school‐bullying perpetration

9.2. School‐bullying victimization outcomes

Overall, we found that antibullying programs significantly reduced bullying victimization under both computational models of meta‐analysis. The effect sizes for each evaluation are presented in Table  10 . The mean summary effect sizes were very similar under both the multivariance adjustment model (MVA: OR = 1.248; 95% CI 1.21–1.29; z  = 12.06; p  < .001; I 2  = 78.327) and the random effects model (RE: OR = 1.244; 95% CI: 1.19–1.31; z  = 8.92; p  < 0.001; τ 2  = 0.032).

Meta‐analysis results: School‐bullying victimization outcomes

This result suggests that students who participated in an antibullying program were significantly less likely to report being bullied by others after receiving the intervention in comparison to students who did not receive the intervention.

The funnel plot in Figure  4 indicates that no publication bias is present in analysis of bullying victimization effect sizes, as the studies fall symmetrically around the mean effect size. Duval and Tweedie's trim and fill procedure highlighted some minor differences between observed effect sizes (OR = 1.245; 95% CI 1.186–1.306; Q  = 460.97) and adjusted effect sizes (OR = 1.241; 95% CI 1.182–1.303; Q  = 473.43). However, this difference is negligible. Based on these results, it was reasonable to assume that publication bias was not likely.

Publication bias analysis: school‐bullying victimization

9.3. Analysis of heterogeneity

In a meta‐analysis, heterogeneity ( Q ) is the between‐study spurious variance that occurs partly because of true variation in effect sizes, but also as a result of random error (Borenstein et al.,  2009 ). Heterogeneity is estimated as the excess variation that exists when we compare the total amount of between‐study variance and within‐study random error.

In the present meta‐analysis, there was significant heterogeneity between studies for both bullying perpetration ( Q  = 323.392; df  = 85; p  < 0.001; I 2  = 73.716) and bullying victimization ( Q  = 387.255; df  = 87; p  < 0.001; I 2  = 77.534) outcomes. Multiple moderator analyses were conducted to explore possible explanations for this heterogeneity.

9.4. Risk of bias analysis

Scores on each of the risk of bias items were summed to estimate a total risk of bias score. This continuous variable was then used to examine the relationship between effectiveness and risk of bias in meta‐regression models.

For perpetration outcomes, risk of bias was not associated with effect size under a random effects model of meta‐regression ( b  = 0.003; SE  = 0.006; z  = 0.50; p  = .621) or under the MVA model ( b  = 0.014; SE  = 0.014; z  = 1.01; p  = .156). Similarly, risk of bias scores did not significantly predict bullying victimization effect sizes under a random effects meta‐regression ( b  = 0.007; SE  = 0.005; z  = 1.30; p  = .195) or the MVA model ( b  = 0.012; SE  = 0.012; z  = 1.006; p  = .157).

9.5. Moderator analyses 8

9.5.1. evaluation method.

Our meta‐analysis further investigated the effectiveness of antibullying programs in relation to the methodological designs used by evaluation studies. The breakdown of results by methodological design is also shown in Tables  9 and  10 for bullying perpetration and victimization outcomes respectively.

Primary studies employing age cohort designs associated with the largest effect sizes for both bullying perpetration (OR = 1.474; 95% CI, 1.39–1.56; p  < .001) and bullying victimization (OR = 1.302; 95% CI, 1.230–1.378; p  < .001) under a random effects model. Similarly, AC studies were associated with the largest effect sizes under the MVA model also (perpetration OR = 1.422; 95% CI, 1.36–1.46; p  < .001) and victimization OR = 1.289; 95% CI, 1.29–1.35; p  < .001).

Under the MVA model of meta‐analysis, mean effect sizes were the same for RCT evaluations (OR = 1.171; 95% CI, 1.08–1.27; p  < .001) and BA/EC evaluations (OR = 1.170; 95% CI, 1.05–1.31; p  = .005) for bullying perpetration outcomes. Moreover, the differences between RCT evaluations (OR = 1.117; 95% CI, 1.03–1.22; p  = .01) and BA/EC evaluations (OR = 1.188; 95% CI, 1.07–1.33; p  = .002) were marginal for bullying victimization outcomes under the MVA model.

In relation to bullying victimization outcomes, before‐after/experimental‐control designs gave the second largest mean effect size (OR = 1.225; 95% CI, 1.085–1.383; p  = 0.001), followed by RCTs (OR = 1.210; 95% CI, 1.091–1.342; p  < .001) under a random effects model. However, the result was the opposite for bullying perpetration outcomes under a random effects model (RCT: OR = 1.244; 95% CI, 1.123–1.379; p  < .001; BA/EC: OR = 1.187; 95% CI, 1.044–1.350; p  = 0.009).

Due to the marginal differences and lack of clear pattern in which method was associated with the largest effect sizes (between RCT and BA/EC) further moderator analysis was not conducted.

9.5.2. Location of intervention

Mean effects for bullying perpetration and bullying victimization outcomes are presented graphically in Figures  5 and  6 , respectively. Table  11 outlines the mean effects for each of the 12 countries for both bullying perpetration and victimization outcomes under both the MVA model and the random effects model.

Forest plot of effect size by location: school‐bullying perpetration

Forest plot of effect sizes by location: school‐bullying victimization

Moderator analyses results: Location of evaluation

Evaluations conducted in Greece were associated with the largest effect sizes for bullying perpetration outcomes, followed by Norway, Italy, United States, and Finland under the MVA model of meta‐analysis. Evaluations conducted in Italy were associated with the largest mean effect sizes in relation to bullying victimization, followed by Spain, Norway, United States, and Finland under the MVA model of meta‐analysis. Additionally, evaluations conducted in Germany and the UK gave significant mean effects when computed using the MVA model.

Under the random effects model, Greek evaluations were similarly associated with the largest effect sizes for bullying perpetration, followed by Spanish and Norwegian evaluations. Evaluations conducted in Italy and the United States were also associated with significant mean effects for reductions in bullying perpetration. In relation to bullying victimization, evaluations conducted in Spain and Italy were associated with very similar mean effect sizes and were the largest of the 12 effect sizes, followed by evaluations conducted in Norway. Evaluations conducted in Australia were also associated with significant mean effects in reducing bullying victimization ( p  < .05) and evaluations conducted in Finland and the United States were nearly statistically significant ( p  = .05 and p  = .06, respectively) under the random effects model.

Due to the large number of different countries and the unequal number of studies in each location, further subgroup analyses were not conducted.

9.5.3. Publication type and year

Table  12 outlines the mean summary effect sizes for each of the publication type moderators for bullying perpetration and victimization outcomes. Evaluations for which data was received via email correspondence from evaluators gave the largest mean effect sizes for both bullying perpetration and bullying victimization. Differences in the mean effect sizes for evaluations reported via unpublished dissertations, either masters or doctoral theses, gave the smallest mean effect sizes for both bullying perpetration and victimization outcomes. Subgroup analysis was not conducted further using these categorizations due to the imbalance in numbers of evaluations in each category (i.e., evaluations were overwhelmingly published in peer‐reviewed journal article format).

Moderator analyses results: Publication type

However, additional analysis was conducted to examine any potential differences between peer reviewed and nonpeer reviewed evaluations. Therefore, the above categories were collapsed, and evaluations reported by dissertation, chapter, correspondence and governmental reports (perpetration n  = 23; victimization n  = 21) were compared to evaluations published via peer‐reviewed journal article.

Under the MVA model, non‐peer‐reviewed evaluations gave a larger (OR = 1.493; 95% CI, 1.266–1.761; p  < .001) mean effect size than peer‐reviewed evaluations (see Table  11 ). Moreover, moderator analysis indicated that the difference was statistically significant ( Q B  = 12.861; df  = 1; p  < .001). However, under the random effects model, both groups gave similar effect sizes for bullying perpetration outcomes, and the difference between peer‐reviewed (see Table  11 ) and non‐peer‐reviewed (OR = 1.309; 95% CI, 1.137–1.508; p  < .001) was not statistically significant ( Q B  = 0.595; df  = 1; p  = .441).

For bullying victimization outcomes, similar results were obtained. Under the MVA model, non‐peer‐reviewed evaluations gave statistically significant larger mean effect sizes (OR = 1.403; 95% CI, 1.262 1.560; p  < .001) than peer‐reviewed evaluations (see Table  11 ; Q B  = 27.197; df  = 1; p  < .001). Yet, there was a marginal difference under the random effects model between peer‐reviewed (see Table  11 ) and non‐peer‐reviewed (OR = 1.231; 95% CI, 1.059–1.431; p  = .007) and the difference was not statistically significant ( Q B  = 0.048; df  = 1; p  = .827).

The mean summary effect size for “2009” studies on the year of publication moderator was OR = 1.487 (95% CI, 1.430–1.546; p  < .001) under the MVA model and OR = 1.411 (95% CI, 1.315–1.513; p  < .001) under the random effects model for bullying perpetration outcomes. Across both computational models these summary effects were larger than those for studies labeled “2016” on bullying perpetration for the MVA model (OR = 1.243; 95% CI, 1.667–1.324; p  < .001) and the RE model (OR = 1.184; 95% CI, 1.087–1.289; p  < .001). Moderator analysis analogous to the ANOVA showed that this difference was statistically significant ( Q B  = 76.412; df  = 1; p  < .001) under fixed effects and mixed effects analysis ( Q B  = 9.676; df  = 1; p  = .002).

In relation to bullying victimization, the mean summary effect size for studies labeled “2009” was larger (OR = 1.322; 95% CI, 1.220–1.432; p  < .001) under the MVA model than the mean summary effect size for studies labeled “2016” (OR = 1.229; 95% CI, 1.175–1.285; p  < .001). Moderator analysis analogous to the ANOVA found that this difference was statistically significant ( Q B  = 10.115; df  = 1; p  = .001) but the difference between odds ratios was marginal. However, under the random effects model the minimal difference between the “2009” studies (OR = 1.215; 95% CI, 1.094–1.350; p  < .001) was not statistically different to the mean summary effect size for “2019” studies (OR = 1.223; 95% CI, 1.139–1.313; p  < .001; Q B  = 0.010; df  = 1; p  = .920).

9.5.4. Intervention program

The mean summary effect sizes for 10 different intervention programs in relation to reducing bullying perpetration behaviors and 9 different intervention programs in relation to reducing bullying victimization behaviors. Table  13 outlines the effectiveness of specific antibullying programs in reducing both school‐bullying perpetration and victimization. The effectiveness of these programs varied greatly.

Moderator analyses results: Intervention program

In relation to school‐bullying perpetration outcomes, the OBPP was associated with the largest mean effect sizes. In addition, evaluations of the OBPP in Norway were associated with larger summary effect sizes than evaluations of OBPP conducted in the United States. However, the difference was not statistically significant for school‐bullying perpetration outcomes when moderator analysis analogous to the ANOVA was conducted ( Q b  = 3.65; df  = 1; p  = 0.06).

Other programs were significantly effective in reducing school‐bullying perpetration behaviors, for example KiVa, Second Step, and Steps to Respect. Positive effect sizes (i.e., OR > 1) were also observed for the BPYS and NoTrap! programs but these effects were not statistically significant in relation to reduction in bullying perpetration outcomes. Negative effects were found for two antibullying programs, the fairplayer manual and ViSC, although these effects were not statistically significant.

In relation to school‐bullying victimization outcomes, NoTrap! was associated with the largest mean effect size, followed by the BPYS Program, and then the OBPP. Our analysis identified that other antibullying programs were also significantly effective in reducing school‐bullying victimization, for example, Steps to Respect and KiVa.

Again, effect sizes for the OBPP varied between evaluations conducted in Norway and evaluations conducted in the United States for bullying victimization outcomes. Moreover, our analysis found that the difference in the magnitude of these effect sizes was statistically significant ( Q b   =  74.95; df  = 1; p  < 0.001). Our analysis also identified negative effects of the Second Step program in relation to bullying victimization outcomes. Evaluations of the ViSC program also had a negative effect on bullying victimization, although this effect was not statistically significant.

9.5.5. Unit of allocation/randomization

Table  14 outlines the mean effects for subgroups of studies according to how participants were allocated to experimental or control groups. Results are presented for bullying perpetration and victimization outcomes for all studies that allocated studies in classes, schools, or individual students. The mean effects for RCT and BAEC for each allocation unit are also presented separately.

Moderator analyses results: Unit of allocation/randomization

In relation to bullying perpetration outcomes, under the MVA model, studies that assigned participants in classes were associated with the largest effect sizes. However, the difference between the mean effect for all evaluations that used classes or schools as the unit of allocation were verging on statistically significance ( Q b   =  3.705, df  = 1, p  = .054). Under the random effects model, evaluations that assigned students to experimental conditions were associated with the largest effect size for bullying perpetration outcomes when all designs were included, and for RCT evaluations and BA/EC evaluations individually. However, the mean effect size for many of the subgroups were not collectively statistically significant overall under the random effects model.

Similarly, under the MVA model, evaluations conducted using a RCT design, and assigned classes to conditions, were associated with the largest effect size for bullying perpetration, although the mean group for this subgroup was not statistically significant. Moreover, moderator analysis analogous to the ANOVA found that the difference in the mean effect size for RCT designs that assigned classes to experimental and control conditions were not statistically different to RCT designs that assigned schools to experimental and control conditions ( Q b   =  1.140, df  = 1, p  = .286 ) .

In relation to BAEC designs, evaluations that assigned students to experimental conditions were associated with the largest mean effect size, although the effect was not statistically significant. However, the difference between the mean effect for BAEC evaluations that assigned classes and those that assigned schools to conditions was statistically significant under the MVA model ( Q b   =  4.551, df  = 1, p  = .033).

For bullying victimization outcomes, studies where the unit of allocation was classes of participants were associated with the largest effect sizes, followed by schools and individual students under the MVA model. The difference between studies that allocated classes and studies that allocated schools was statistically significant ( Q b   =  12.450, df  = 1, p  < .001). This pattern was observed when all designs were included, and for the subgroup of RCT evaluations and the subgroup of BA/EC evaluations. Thus, when participants were assigned in classes the mean effect size for these RCT evaluations were significantly associated with larger effect sizes ( Q b   =  13.590, df  = 1, p  < .001) for reductions in bullying victimization than RCT evaluations that assigned schools. Yet the difference between the mean effect sizes for BA/EC evaluations that assigned classes were not statistically significant ( Q b   =  3.359, df  = 1, p  = .067) than BA/EC evaluations that assigned schools to experimental conditions.

9.5.6. Conflict of interest

COI was a categorical moderator variable with three levels: high‐risk (H), low‐risk (L), and possible‐risk (P). Moderator analysis analogous to the ANOVA was conducted so as to assess the differences between evaluations on each level. Studies categorized as possible‐risk on COI variable were excluded from subgroup comparisons to establish the differences between evaluations that were clearly high‐risk and evaluations that were clearly low‐risk. Table  15 outlines the mean summary effects for each group for both bullying perpetration and bullying victimization outcomes.

Moderator analyses results: Conflict of interest

Note : Four studies and six studies were excluded from the present moderator analysis for perpetration and victimization outcome respectively as not enough information was available.

Moderator analyses found that the difference between high‐risk and low‐risk studies on COI variable was statistically significant for bullying perpetration outcomes under both the MVA model ( Q B  = 50.129; df  = 1; p  < .001) and the random effects model ( Q B  = 4.900; df  = 1; p  = .027). This suggests that evaluations considered to have high COI were associated with larger overall effect sizes for bullying perpetration. Similarly, high‐risk COI studies were significantly associated with slightly larger effect sizes for bullying victimization in comparison to low‐risk COI studies when compared under both the MVA model ( Q B  = 16.127; df  = 1; p  < .001) and the random effects model ( Q B  = 4.449; df  = 1; p  = .035).

9.5.7. Program specificity

The majority of evaluations included in our meta‐analysis were of highly specific intervention programs, that is, those that targeted bullying behaviors and no other outcomes. Consistently across computational model and both perpetration and victimization outcomes these subgroups were associated with the largest mean effect sizes. These results are presented in Table  16 . Additionally, highly specific programs were the only subgroup of evaluations that gave a statistically significant mean summary effect under both the MVA model and the random effects model for bullying victimization outcomes. In relation to bullying perpetration outcomes, the subgroup of evaluations that were coded as “medium” on the program specificity moderator were associated with a statistically significant mean effect size under the MVA model ( p  < .001) and the random effects model ( p  = .036).

Moderator analyses results: Program specificity

10. DISCUSSION

10.1. summary of main findings.

Overall, our updated meta‐analysis found that school‐based antibullying programs are effective in reducing both school‐bullying perpetration and victimization. For school‐bullying perpetration the weighted mean OR = 1.324 under the MVA model, or OR = 1.309 under a random‐effects model (RE) were associated with reductions of approximately 19–20%. 9 In comparison, the weighted mean ORs for bullying victimization outcomes were 1.248 and 1.242 under the MVA model and the random effects model respectively. These mean effect sizes correspond to an approximate reduction in bullying victimization of 15–16%. These results suggest that the included interventions were slightly more effective at reducing school‐bullying perpetration than school‐bullying victimization.

The results of this meta‐analysis are consistent with findings from most of previous reviews that indicate that antibullying programs have a small but significant effect, with some variations in overall results being attributable to methodological differences in inclusion and exclusion criteria (Ttofi et al.,  2014 ). Our mean effect sizes are also consistent with the earlier review (Farrington & Ttofi, 2009 ; Ttofi & Farrington,  2011 ), although the differences further outline that moderator variables such as methodological design may be responsible for variability. For example, the weighted mean effect sizes for both bullying perpetration and bullying victimization outcomes estimated in the earlier Campbell report were larger than those estimated in the present report.

Yet, we included publication year as a categorical moderator variable in the present analysis. We found that more recent studies (i.e., those that were not included by Farrington & Ttofi,  2009 ) were significantly different to studies that were included in the earlier review. Namely, recent studies were actually associated with significantly larger effect sizes for both bullying perpetration and victimization outcomes (see Section 8.5.3).

Therefore, as we excluded studies considered to have utilized less scientifically rigorous methodological designs this may explain the differences in the weighted mean effect sizes. Specifically, we excluded evaluations conducted using “other experimental‐control designs,” described in the earlier review as evaluations in which participants were assigned to experimental and control conditions but bullying outcomes were only measured after implementation of the intervention. Thus, attributing any change in behaviors to the intervention is potentially risky because there may be other reasons why a positive effect of the intervention was observed. For example, the experimental and control groups were not comparable at baseline, but this remains unknown as no measure of bullying was obtained.

Thus, the inclusion of these less methodologically rigorous evaluations may explain why the weighted mean effects sizes reported in the earlier review were larger than those reported in the current report, but our moderator analysis found a contradictory pattern. The following sections of this report will aim to discuss the findings obtained by our moderator analyses and also the strengths and limitations of the current analysis and potential avenues for future research. The heterogeneity in this meta‐analysis was very large for both bullying perpetration and victimization outcomes. This may suggest that there was a wide range of effects across programs and we may not be able to explain differences using moderator analysis.

10.2. Moderator analyses

10.2.1. evaluation method.

Under both the MVA and random effects models, evaluations conducted using age cohort designs were identified to be, collectively, the most effective, or at least associated with the largest mean effect sizes. This is consistent with Farrington and Ttofi's ( 2009 ) review. This methodological design was first introduced as an evaluation design for the OBPP (Olweus,  1991 ). This approach has been criticized for the potential threats to internal validity, history and testing effects (Farrington & Ttofi,  2009 , p. 15). It has been suggested that this design avoids the threats of aging and maturation effects, as individuals within the same school act as a control group for same‐aged experimental participants (Olweus,  2005a ). However, this design is vulnerable to cross‐contamination between experimental and control participants which would impact the overall effectiveness. Notably, intervention researchers have tested the OBPP with other methodological designs (e.g., Bauer et al.,  2007 ) which resulted in smaller effects.

Interestingly, the pattern between RCTs and BA/EC designs was less clear. In relation to bullying victimization outcomes, evaluations using BA/EC designs appear to be more effective than evaluations using RCT designs. However, for bullying perpetration outcomes, evaluations using RCT designs appear to be more effective than evaluations that utilized BA/EC designs. Further research is needed to understand these effects. However, the nature of these analyses is correlational and the differences between effect sizes are marginal. Thus, no concrete conclusion can be drawn in relation to the association between randomized and nonrandomized quasi experimental designs and effect size in the present context.

10.2.2. Unit of allocation/randomization

In theory, RCTs are the best method of evaluation of interventions because random allocation ensures that any observed differences between experimental and control groups occurs as a result of experimental manipulation, thus giving the best possible internal validity (Farrington,  1983 ,  2003 ). However, the unit of random allocation can have an impact on internal validity. For example, we assume that individuals are randomly assigned to experimental and control conditions, so that RCT designs adequately account for the random variation that occurs in real‐world research (Weisburd,  2003 ).

However, in practice, evaluations of antibullying programs may be more likely to assign groups of individuals, for example in terms of classrooms or schools, to experimental conditions rather than individual students. This is true for both randomized (e.g., classrooms, Chaux et al.,  2016 ; or schools, Espelage et al.,  2015 ) and nonrandomized (e.g., classrooms, Ortega‐Ruiz et al.,  2012 ; or schools, Rawana et al.,  2011 ) methodologies. When this is the case, we need larger numbers to ensure adequate statistical conclusion validity and avoid issues of selection effects and differential attrition (Farrington & Ttofi,  2009 ; Ttofi & Farrington,  2011 ). There was a lot of variation in the unit of allocation in our primary studies, which may explain why we did not find that one methodological design was more effective than another.

Moreover, the majority of included evaluations did not use the same unit for allocation and analysis, thus, posing a threat to our results. We approach the results therefore with caution, favouring more conservative estimates. Furthermore, the relationship between the unit of randomization/allocation moderator variable and the effect sizes for bullying perpetration and victimization outcomes was unclear. Whether or not the differences between subgroups of evaluations that assigned classes or schools to experimental conditions were statistically significant or not depended on the computational model used and the bullying outcome in question. For bullying perpetration, the differences between studies based on unit of allocation were not statistically significant for randomized and nonrandomized studies. For bullying victimization outcomes, studies where classes were the unit of allocation were associated with the largest effect sizes when all designs where included and for randomized evaluations, but not for nonrandomized evaluations, separately.

Risk of bias analysis also found that a large number of RCT studies were categorized as being high risk for allocation‐related items on the EPOC tool. Therefore, the differences observed between primary evaluations in our meta‐analysis may be due to the observation that largely the unit of allocation and the unit of analysis were not the same in primary studies. However, further analysis and investigation is needed to better understand these results.

10.2.3. Location of intervention

Overall, the results of our meta‐analysis are consistent with previous findings and show that school‐based antibullying programs have a modest but significant effect in reducing bullying behaviors. However, our meta‐analysis included evaluations of antibullying programs from a wide range of countries and specific intervention programs, far more than previous meta‐analyses (e.g., Cantone et al.,  2015 ; Chalamandaris & Piette,  2015 ; Evans et al.,  2014 ; Jiménez‐Barbero et al.,  2012 ,  2016 ). As a result, the results of this meta‐analysis are robust and have implications for bullying research globally.

Our analysis identifies that antibullying programs worldwide are effective in reducing school‐bullying perpetration and victimization by significant amounts. Moreover, evaluations in different countries appear to vary in effectiveness. In Greece, where evaluations included in our meta‐analysis were associated with the largest effect sizes, school‐bullying perpetration behaviors were reduced by approximately 40%. Evaluations conducted in the Norway, Italy and the United States were also effective in reducing bullying perpetration by approximately 21–25%.

Antibullying programs implemented and evaluated in Italy were associated with the largest reduction in school‐bullying victimization in our meta‐analysis, with the odds ratio effect size corresponding to an approximate reduction of 31%. Moreover, evaluations conducted in Spain and Norway reduced school‐bullying victimization by approximately 28% and 23%, respectively. Evaluations conducted in Finland, Germany and the UK were also significantly effective, although less so, reducing school‐bullying victimization by approximately 8–12%.

There are many potential explanations for the differences in effectiveness observed between countries. For example, definitions of school‐bullying, and behaviors that constitute bullying, differ between countries. Previous research conducted by Smith et al. (2000) showed that school‐bullying is perceived differently across different countries and cultures and this may explain variability in bullying reporting. Definitions of school bullying, and behaviors that constitute bullying, differ between countries. For example, Smith et al. ( 2016 ) showed that school bullying in Eastern cultures manifests more often as exclusion or isolation of an individual victim. In comparison, school bullying in Western cultures comprises a wider range of physical, verbal and relational forms of aggression.

Our meta‐analysis included several examples of cases where the same intervention program was evaluated in different countries (e.g., KiVa program in Finland (Kärnä et al.,  2013 ) and in Italy (Nocentini & Menesini, 2016)). While societal practices, educational systems, and individual lifestyles may differ greatly, some argue that there may be some support for the cross‐national applicability of specific intervention programs. However, there is a current lack of existing research comparing the effectiveness of specific interventions in specific countries.

Previous research has indicated that are also cultural differences in bullying behaviors among adolescents (e.g., Smith et al.,  2016 ). As such, an antibullying program to reduce these behaviors may be impacted by these differences. This is particularly evident when we observe the variations in effect sizes for the OBPP (Olweus,  1993 ) and the KiVa antibullying program. These programs may be the most well‐known antibullying programs that are commercially available, and as such as the only examples in our review of interventions evaluated in completely different locations.

The OBPP program was originally designed and implemented in Norway, and it is therefore not surprising that the OBPP program appears to be effective in reducing both school‐bullying perpetration and victimization when evaluated in Norway, compared to evaluations in the United States (see Table  13 ). While the program was still significantly effective in the United States, the percentage decrease in school‐bullying perpetration was roughly 25% and in school‐bullying victimization was roughly 11%. These figures are lesser in comparison to the decreases in bullying behaviors seen in Norwegian evaluations (35% perpetration; 29% victimization). These differences could be attributed to different evaluation methodologies (see Gaffney et al., 2019), however, they most likely reflect cultural and societal differences between youth in Norway and youth in the United States.

Interestingly, the opposite is observed with the KiVa program. When KiVa was evaluated in Finnish samples, the program was effective in reducing school‐bullying perpetration by approximately 4–5% and school‐bullying victimization by approximately 6% (Kärnä et al.,  2011a ,  2011b ,  2013 ). However, when evaluated in Italian primary and secondary schools, the effect sizes were much larger. Nocentini and Mensini (2016) found that KiVa was effective in reducing school‐bullying perpetration by approximately 15–20% and school‐bullying victimization by approximately 25%.

In the case of KiVa, each of the evaluations used the same methodology (i.e., RCT), but varied greatly in the sample size. Thus, further research is needed to explain why some interventions (e.g., OBPP or KiVa) appear to be more effective in some samples compared to others. The programs are still effective, but the variation in effect size could be attributable to a number of different methodological and implementation factors that warrant further exploration.

10.2.4. Intervention program

Following this logic, we also explored the effectiveness of the specific antibullying programs. Out of the four most widely disseminated antibullying programs included in our review (i.e., KiVA, NoTrap!, OBPP, ViSC), the OBPP was collectively the most effective in reducing school bullying perpetration of these. Across 11 evaluations, the OBPP reduced bullying perpetration by approximately 26%, which was larger than any other widely disseminated program.

In relation to school‐bullying victimization outcomes, the NoTrap! program was the most effective, reducing victimization by around 37%. NoTrap! also reduced bullying perpetration by a considerable amount, approximately 22%, but this effect was not statistically significant. The KiVA program, significantly reduced school bullying perpetration by approximately 9% and school bullying victimization by approximately 11%. The ViSC program was the only program to increase bullying perpetration (by roughly 4%) and bullying victimization (by roughly 4%) although these effects were not statistically significant.

Another moderator we used to code differences between included evaluations was the specificity of the intervention program. In other words, we evaluated each intervention program on how specific it related to bullying behaviors. Unsurprisingly, our findings suggest that antibullying programs gave the largest overall effect sizes. While the significance of the differences between subgroups was not computed due to the large discrepancies between the numbers of evaluations included in each subgroup.

However, our inclusion criteria for the current report was strictly concerned with school‐bullying intervention programs and behavioral outcomes of bullying. As such, we may have overlooked effective programs that only included nonbehavioral outcomes of bullying (e.g., attitudes toward bullying, awareness of bullying) or other problem behaviors (e.g., peer aggression or victimization, mental health issues, juvenile delinquency, etc.) that occur among young people in schools. Changes in these behaviors may also impact bullying, either directly or indirectly, yet, more research is needed to understand this potential effect. Most obvious in the present report is how programs that target specifically school‐bullying may impact cyber‐bullying, and vice versa, given the significant overlap in the prevalence of these behaviors (Baldry et al.,  2017 ).

Further research is also needed to better understand specifically “what works” in these “specific interventions.” In the previous review, (Farrington and Ttofi  2009 ; Ttofi & Farrington,  2011 ) conducted detailed coding of interventions and evaluations and analyzed how effect sizes varied between components and features of primary studies. For example, parent training, playground supervision, and more intense and longer programs were significantly correlated with larger reductions in bullying perpetration (Ttofi & Farrington,  2011 ). Moreover, several intervention components were associated with larger reductions in bullying victimization (e.g., videos, disciplinary methods, co‐operative group work and more intense and longer programs). Therefore, an important avenue for future research is to assess the differences in effectiveness of antibullying programs according to specific intervention components across the 100 evaluations included in our meta‐analysis. Such research would have important implications for policy and the development of future antibullying programs.

Additionally, it appears that since 2009 several large‐scale antibullying programs have been implemented and evaluated (e.g., KiVa; Kärnä et al.,  2013 ; NoTrap!; Menesini et al.,  2012 ; Palladino et al.,  2016 ). Because there is typically more information available on the specific components of these programs, we may be able to code more specific details in future analyses. For example, many studies may fit the criteria for “parent training,” but there is a significant difference between the intensity of parental involvement. For example, some studies may include parents merely by sending letters home with participant children (e.g., Brown et al.,  2011 ), while others include parents more actively by holding information evenings or requiring children to complete take‐home tasks with parental involvement (e.g., Berry & Hunt,  2009 ; Domino,  2013 ).

Earlier research highlighted how varying levels of implementation of each intervention component may explain variability in intervention outcomes (Bloom et al.,  2003 ). Interestingly, a narrative review by Smith et al. ( 2003 ) reported that although 14 whole‐school antibullying programs obtained modest effects overall, those that monitored implementation obtained twice the mean effects on self‐reported rates of bullying and victimization than those that did not monitor implementation. Thus, additional analyses are required to better understand specifically what works in existing antibullying programs and the underlying mechanisms of behavioral change

10.2.5. COI and publication type

Possibly the most conclusive results from our moderator analyses were observed in relation to COI and publication type. First, across both computational models and outcomes, studies that were categorized as being high‐risk for COI were associated with significantly larger reductions in bullying perpetration and victimization. Second, under the MVA model of meta‐analysis, non‐peer‐reviewed evaluations were associated with significantly larger reductions in both bullying perpetration and victimization outcomes. However, the same results were not observed under the random effects.

We examined COI in terms of the involvement of the program developer in the evaluation. Our results may indicate possible sources of biases. For example, it may be that when the individual, or team, that are credited with developing an antibullying program are also involved in the evaluation of said intervention, biases such as confirmation bias may impact the results. However, it may not be a perceivably “negative” source of bias. Perhaps, when the program developer is involved in the implementation of the program, the intervention is simply delivered better and more effectively. There are a number of other factors that could also be affected and in turn impact the effect size, such as teacher and staff efficacy and motivation to participate the in the program.

There are more sophisticated measures of COI (e.g., Eisner et al.,  2012 ) that include elements such as whether or not the evaluator could potentially benefit financially from the intervention program. Further indicators of COI are thus needed to better understand the impact on evaluation results. For example, our findings in relation to COI and larger effect sizes may be explained as: evaluations in which the program developer was included appear to be more effective because of the expertise and intricate knowledge of the developer. Therefore, the results may reflect differences in the quality of program implementation rather than troublesome biases. Additional research is needed.

10.3. Limitations and avenues for future research

Like most meta‐analyses, the current report is largely limited by the lack of understanding as to what is the “true effect.” When comparing mean effect sizes between moderators for example, it is difficult to determine the validity of the result. Throughout our discussion of result we discuss that one subgroup of studies was associated with larger or smaller effect sizes than another, and the statistical significance of these differences. Thus, we avoid saying studies in subgroup A (e.g., evaluations conducted in Greece) are more effective than studies in subgroup B (e.g., evaluations conducted in Italy). Due to the correlational nature of our moderator analyses we cannot make causal inferences. In addition to this limitation, and those previously discussed (Section  9.2 ), the following section of this report discusses some further limitations.

10.3.1. Measurement of bullying

Experts in the area of school‐bullying research have outlined how there still remain issues of comparability in the assessment of school‐bullying perpetration and victimization (Volk et al.,  2017 ). Studies included in the present meta‐analysis used a wide variety of quantitative measures of school‐bullying behaviors, including self‐report measures (e.g., the Revised Olweus Bully/Victim Questionnaire—Olweus,  1986 ,  1996 ), or peer‐report measures (e.g., the Participant Role Questionnaire—Salmivalli et al.,  1996 ). One issue that arises is that the timeframe within which participants are required to indicate the frequency of bullying can vary greatly. One scale may ask about bullying experiences within the last 3 months, while another may ask about ever having experienced, or participated in, school‐bullying. Moreover, included studies utilized a mixture of continuous or dichotomous measures of school‐bullying, and the cut‐off points used to categorize someone as either a bully, victim, or not‐involved also varied.

Furthermore, the majority of evaluations included in our analysis reported bullying outcomes at different time points, largely, before implementation, after implementation, with a possible additional follow‐up time point. However, we computed effect sizes using measures of bullying taken before implementation and immediately post implementation of the intervention. Therefore, we cannot generalize results to the long‐term effectiveness of antibullying programs, or any potential influence of dose‐response effect. Future research should aim to examine the longitudinal effectiveness of interventions to reduce bullying perpetration and victimization in the long‐term.

When conducting our systematic searches for the present review, we did not set restrictions based on measurement issues, other than including quantitative measures of school‐bullying behaviors. However, types of reports, for example, could influence the overall effectiveness effect size. This may possibly explain why our meta‐analysis found that programs are more effective in reducing bullying perpetration outcomes. For example, if programs are concerned with raising awareness about bullying and the associated negative impact on victims, participants who reported bullying perpetration before the intervention may be less likely to self‐report bullying behaviors after completing the program. As a result, the intervention may be perceived as being effective, but the change in reports of bullying may have been a result of social desirability responding (He et al.,  2015 ; Rigby & Johnson,  2006 ). Conversely, raising awareness on the negative impact of school bullying may lead to increased reporting of victimization due to sensitization effects (Stevens et al.,  2000 ). Notably, sensitization effects due to raised awareness may affect not only self‐report data but also peer nomination data and teacher reports (Smith et al.,  2003 , p. 597). Therefore, future research could aim to examine whether the style of report used, differing cut‐off points and varying timeframes affect estimations of intervention effectiveness.

10.3.2. Cyberbullying behaviors

Another key limitation of the present review is the omission of cyberbullying behaviors. Prominent researchers in the area have argued that cyberbullying behaviors do not warrant a completely separate line of study, because of the significant overlap between offline and online bullying (Olweus & Limber,  2017 ). A recent meta‐analysis of cyberbullying intervention and prevention programs found that, out of studies assessing various facets of cyberbullying, a large number were concerned with this overlap (Gaffney et al., 2019). The Gaffney et al. (2019) meta‐analysis concluded that anticyberbullying programs were effective in reducing cyberbullying perpetration by roughly 9–15% and cyberbullying victimization by roughly 14–15%. As illustrated in that other review, there is a need for future research to assess the effectiveness of intervention programs that target both online and offline bullying concurrently. As a result of the significant overlap (e.g., Waasdorp & Bradshaw, 2015), it is important for policy makers, researchers, and program developers to know whether or not these forms of aggressive behaviors should be targeted together or individually. Future research should aim to examine the effectiveness of programs designed to reduce school‐bullying on cyberbullying outcomes, and vice versa. Additional analysis to examine the differences between programs that target offline and online behaviors concurrently in terms of effectiveness to reduce both school‐ and cyber‐bullying is also needed.

10.3.3. Models of meta‐analyses

The current report presents findings using two computational models of meta‐analyses: the random effects model and the multiplicative variance adjustment model. While, the random effects model is often suggested as the preferred model for meta‐analyses in social sciences, for reasons already discussed (Section  7.3 ), this approach is also limited. However, even though many meta‐analyses in medical sciences (e.g., Ayieko et al.,  2014 ; Dorjee et al.,  2018 ; Woolf‐King et al.,  2013 ) have used the MVA model as an alternative method of accounting for between‐study heterogeneity in weighted mean effect sizes, this model is yet to be widely accepted in behavioral sciences. A number of recent publications (e.g., Portnoy & Farrington,  2015 ; Zych et al.,  2019 ) have begun to use the MVA model.

It is evident in the current report that the results are influenced by the computational model used. The overall mean effect sizes for bullying perpetration and victimization were not that different under both models but the results of moderator analyses were greatly influenced by how we accounted for the between‐study heterogeneity. Further research is needed in order to examine the reasons for this and also evaluate how best to choose an appropriate computational model when conducting a meta‐analysis.

10.4. Concluding remarks

This report presents an updated systematic and meta‐analytical review of the effectiveness of school‐bullying intervention and prevention programs. Overall, our review found that school‐based antibullying programs are effective in reducing both bullying perpetration and bullying victimization, and that effect sizes can vary according to several moderator variables. However, further research is needed to better understand the reasons for variation in observed effect sizes. Research is needed to investigate the specific components of antibullying programs that work best to reduce bullying behaviors. The results of our meta‐analysis have important implications for policy and the development of future antibullying programs, but future research should aim to better understand the effective mechanisms in bullying intervention and prevention.

11. TECHNICAL APPENDICES

11.1. calculating the before‐after intervention effect.

Williams et al. ( 2015 ) evaluated the effectiveness of the Start Strong program based on students' self‐reported experiences of bullying victimization. The primary study found that, at baseline, 23% of participants in the experimental group ( N  = 717) reported bullying victimization, while 23% of participants in the control group ( N  = 800) also reported bullying victimization at baseline. Hence, the baseline OR was calculated as follows (Table  17 ):

Data used to estimate baseline odds ratio

Thus, the OR before  = 0.999, Ln OR before  = −0.002, and var Ln OR before  = 0.015. Williams et al. ( 2015 ) report that after implementation of the Start Strong program, bullying victimization was reported by 28% of experimental participants and 34% of control participants. Accordingly, the posttest OR was calculated as follows (Table  18 ):

Data used to estimate postintervention odds ratio

Thus, the OR after  = 1.323; Ln OR after  = 0.28; and var Ln OR after  = 0.013. Employing these figures, the ln OR for the intervention effect of the Start Strong program was calculated as:

The ln OR change is computed as the difference between the before and after effect size and the variance of this new estimate is adjusted by multiplying the sum of the variances of before and after variances by 0.75. This is an approximation of the assumed correlation between before and after effect sizes. The ln OR change and the SE of ln OR change were then entered into CMA as an estimation of the intervention effect.

11.2. Multiplicative variance adjustment

In the present meta‐analysis, the summary effect size estimated for bullying perpetration was OR = 1.324 with 95% confidence intervals of 1.298–1.351 under a fixed effects model. The effect size in the MVA model is the same as the effect size in the fixed effects model. The variance of the effect size in the MVA model is calculated as follows:

Therefore, in the above example of the summary effect size for bullying perpetration outcomes, the FE var is 0.000104. Therefore, with Q  = 458.555 and df  = 109, the MVA adjustment for fixed effects is 0.02098, calculated as:

Therefore, the adjusted standard error is 0.0209. In this example thus, the MVA fixed effect is OR = 1.324, and the 95% confidence intervals are 1.271–1.380.

11.3. Odds ratio to percentage conversion

The conversion from weighted mean odds ratio to percentage value is also described in the previous Campbell report (see Farrington & Ttofi, 2009 ). The formula involves assuming equal allocation of participants to experimental and control conditions and that the % of bullies and/or victims was lesser in the experimental condition than in the control condition (as supported by our overall positive mean effect size).

For example, if there are 200 participants in each experimental condition and approximately 30% of participants report bullying victimization in the control condition and 25% victims in the experimental condition, the numbers of victims and nonvictims would be as follows: (Table  19 ).

Data used to convert odds ratio to percentage

Therefore using the previously described formula for estimating an odds ratio, the following data would correspond to an odds ratio of 1.286 (i.e., [150 × 60]/[140 × 50]). Moreover, the percentage decrease would be approximately 16.67% (i.e., (10/60) × 100).

Using this basic formula, we can manipulate the % and number of victims in each experimental condition in order to achieve a odds ratio that corresponds to our weighted mean effect size (i.e., MVA: OR = 1.324 and RE: OR = 1.309 for bullying perpetration; MVA: OR = 1.248 and RE: OR = 1.242 for bullying victimization). Using the n values that give the closest possible mean effect size we can thus estimate the corresponding percentage reduction in either bullying perpetration or victimization outcomes.

APPENDIX A. 

Appendix: full search syntax, database: web of science.

Bully* AND Intervention AND Evaluation

Anti‐Bullying AND School AND Program* AND Evaluation

Anti‐Bully* AND Program* AND Outcome

Bully‐victim AND Prevention AND Evaluation

Bully* AND School AND Intervention

Bully* AND School AND Prevention

Database: Scopus

Bully* AND School AND Program*

Bully* AND School AND Evaluation

Bully* AND School AND Intervention AND Evaluation

Bully* AND School AND Prevention AND Evaluation

Anti‐bullying AND Program* AND Evaluation

Database: National Criminal Justice Reference Service

Bully* AND Prevention AND Evaluation

Anti‐bullying AND Program* AND Effect*

Database: PsycINFO

Bully* AND Intervention AND Program* AND Evaluation

Bully* AND Prevention AND Program* AND Effect*

Database: Cochrane Controlled Trials Register

Bully* AND Intervention AND Program*

Bully* AND Prevention AND Program AND Evaluation

Database: British Education Index

Bully* AND Prevention AND Program* AND Evaluation

Bully* AND Intervention AND Program* AND Effect*

Database: Embase

Database: medline, database: eric & criminal justice abstracts.

www.scholar.google.co.uk

APPENDIX B. 

Appendix: risk of bias results for included studies.