communication of research findings definition

Communicating Research Findings

• First Online: 03 January 2022

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• Rob Davidson 5 &
• Chandra Makanjee 6  

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Research is a scholarship activity and a collective endeavor, and as such, its finding should be disseminated. Research findings, often called research outputs, can be disseminated in many forms including peer-reviewed journal articles (e.g., original research, case reports, and review articles) and conference presentations (oral and poster presentations). There are many other options, such as book chapters, educational materials, reports of teaching practices, curriculum description, videos, media (newspapers/radio/television), and websites. Irrespective of the approach that is chosen as the mode of communicating, all modes of communication entail some basic organizational aspects of dissemination processes that are common. These are to define research project objectives, map potential target audience(s), relay target messages, define mode of communication/engagement, and create a dissemination plan.

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Chandra Makanjee

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Robert Davidson

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Davidson, R., Makanjee, C. (2021). Communicating Research Findings. In: Seeram, E., Davidson, R., England, A., McEntee, M.F. (eds) Research for Medical Imaging and Radiation Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-79956-4_7

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Communicating and disseminating research findings to study participants: Formative assessment of participant and researcher expectations and preferences

Affiliations.

• 1 College of Medicine, Medical University of South Carolina, Charleston, SC, USA.
• 2 College of Health Professions/Healthcare Leadership & Management, Medical University of South Carolina, Charleston, SC, USA.
• 3 South Carolina Clinical & Translational Research Institute (CTSA), Medical University of South Carolina, Charleston, SC, USA.
• 4 SOGI-SES Add Health Study Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
• 5 College of Nursing, Medical University of South Carolina, Charleston, SC, USA.
• PMID: 32695495
• PMCID: PMC7348011
• DOI: 10.1017/cts.2020.9

Introduction: Translating research findings into practice requires understanding how to meet communication and dissemination needs and preferences of intended audiences including past research participants (PSPs) who want, but seldom receive, information on research findings during or after participating in research studies. Most researchers want to let others, including PSP, know about their findings but lack knowledge about how to effectively communicate findings to a lay audience.

Methods: We designed a two-phase, mixed methods pilot study to understand experiences, expectations, concerns, preferences, and capacities of researchers and PSP in two age groups (adolescents/young adults (AYA) or older adults) and to test communication prototypes for sharing, receiving, and using information on research study findings.

Principal results: PSP and researchers agreed that sharing study findings should happen and that doing so could improve participant recruitment and enrollment, use of research findings to improve health and health-care delivery, and build community support for research. Some differences and similarities in communication preferences and message format were identified between PSP groups, reinforcing the best practice of customizing communication channel and messaging. Researchers wanted specific training and/or time and resources to help them prepare messages in formats to meet PSP needs and preferences but were unaware of resources to help them do so.

Conclusions: Our findings offer insight into how to engage both PSP and researchers in the design and use of strategies to share research findings and highlight the need to develop services and support for researchers as they aim to bridge this translational barrier.

Keywords: Communication; dissemination; research findings; research participant preference; researcher preference.

© The Association for Clinical and Translational Science 2020.

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Communicating Research Findings

This course focuses on effective practices, guidelines, and strategies for communicating and presenting research findings.

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Researchers have a range of ethical obligations when designing and conducting their research activities.  These obligations also pertain to how researchers share their findings with others. This course covers two main topic areas in the realm of sharing research findings. The first module focuses on practices and guidelines for presenting research findings to other researchers. The second module focuses on strategies for more effectively communicating research to the public.

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Communicating with the public.

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Discusses how to present research findings accurately for publication, including strategies for preparing figures, graphs, and digital images.

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How to disseminate your research

Published: 01 January 2019

Version: Version 1.0 - January 2019

This guide is for researchers who are applying for funding or have research in progress. It is designed to help you to plan your dissemination and give your research every chance of being utilised.

What does NIHR mean by dissemination?

Effective dissemination is simply about getting the findings of your research to the people who can make use of them, to maximise the benefit of the research without delay.

Research is of no use unless it gets to the people who need to use it

Professor Chris Whitty, Chief Scientific Adviser for the Department of Health

Principles of good dissemination

Stakeholder engagement: Work out who your primary audience is; engage with them early and keep in touch throughout the project, ideally involving them from the planning of the study to the dissemination of findings. This should create ‘pull’ for your research i.e. a waiting audience for your outputs. You may also have secondary audiences and others who emerge during the study, to consider and engage.

Format: Produce targeted outputs that are in an appropriate format for the user. Consider a range of tailored outputs for decision makers, patients, researchers, clinicians, and the public at national, regional, and/or local levels as appropriate. Use plain English which is accessible to all audiences.

Utilise opportunities: Build partnerships with established networks; use existing conferences and events to exchange knowledge and raise awareness of your work.

Context: Understand the service context of your research, and get influential opinion leaders on board to act as champions. Timing: Dissemination should not be limited to the end of a study. Consider whether any findings can be shared earlier

Remember to contact your funding programme for guidance on reporting outputs .

Your dissemination plan: things to consider

What do you want to achieve, for example, raise awareness and understanding, or change practice? How will you know if you are successful and made an impact? Be realistic and pragmatic. 

Identify your audience(s) so that you know who you will need to influence to maximise the uptake of your research e.g. commissioners, patients, clinicians and charities. Think who might benefit from using your findings. Understand how and where your audience looks for/receives information. Gain an insight into what motivates your audience and the barriers they may face.

Remember to feedback study findings to participants, such as patients and clinicians; they may wish to also participate in the dissemination of the research and can provide a powerful voice.

When will dissemination activity occur? Identify and plan critical time points, consider external influences, and utilise existing opportunities, such as upcoming conferences. Build momentum throughout the entire project life-cycle; for example, consider timings for sharing findings.

Think about the expertise you have in your team and whether you need additional help with dissemination. Consider whether your dissemination plan would benefit from liaising with others, for example, NIHR Communications team, your institution’s press office, PPI members. What funds will you need to deliver your planned dissemination activity? Include this in your application (or talk to your funding programme).

Partners / Influencers: think about who you will engage with to amplify your message. Involve stakeholders in research planning from an early stage to ensure that the evidence produced is grounded, relevant, accessible and useful.

Messaging: consider the main message of your research findings. How can you frame this so it will resonate with your target audience? Use the right language and focus on the possible impact of your research on their practice or daily life.

Channels: use the most effective ways to communicate your message to your target audience(s) e.g. social media, websites, conferences, traditional media, journals. Identify and connect with influencers in your audience who can champion your findings.

Coverage and frequency: how many people are you trying to reach? How often do you want to communicate with them to achieve the required impact?

Potential risks and sensitivities: be aware of the relevant current cultural and political climate. Consider how your dissemination might be perceived by different groups.

Think about what the risks are to your dissemination plan e.g. intellectual property issues. Contact your funding programme for advice.

More advice on dissemination

We want to ensure that the research we fund has the maximum benefit for patients, the public and the NHS. Generating meaningful research impact requires engaging with the right people from the very beginning of planning your research idea.

More advice from the NIHR on knowledge mobilisation and dissemination .

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Communication and Reporting of Research Findings

Communication and Reporting of Research Findings in a research and needs your check.

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Present or publish your research or creative activity, what is research communication.

"The ability to interpret or translate complex research findings into language, format, and context that non experts understand" (IDS 2011).

Research Communication

Research: Discovering new knowledge

Communication: The exchange of information

Research Dissemination vs. Research Communication: What is the difference?

Research communication incorporates the dissemination process but doesn't stop there! The process of tailoring your message for your audience is the hallmark of effective research communication .

Why should Research Communication matter..

Professional development

Improved communication skills

experiencing the real world of researching as well as presenting 

To the general public:

Improve the quality of life

Help with miscommunication and misconceptions

Increase interest and participation in the research field especially in the underrepresented social groups

To the research community:

Increase knowledge and further implement research in the future

Strong impacts in the research and science fields

Lead to new collaborations

The 7 C's of Communication

A checklist for communication:

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• Systematic Review
• Open access
• Published: 22 November 2010

Disseminating research findings: what should researchers do? A systematic scoping review of conceptual frameworks

• Paul M Wilson 1 ,
• Mark Petticrew 2 ,
• Mike W Calnan 3 &
• Irwin Nazareth 4  

Implementation Science volume  5 , Article number:  91 ( 2010 ) Cite this article

86k Accesses

137 Citations

20 Altmetric

Metrics details

Addressing deficiencies in the dissemination and transfer of research-based knowledge into routine clinical practice is high on the policy agenda both in the UK and internationally.

However, there is lack of clarity between funding agencies as to what represents dissemination. Moreover, the expectations and guidance provided to researchers vary from one agency to another. Against this background, we performed a systematic scoping to identify and describe any conceptual/organising frameworks that could be used by researchers to guide their dissemination activity.

We searched twelve electronic databases (including MEDLINE, EMBASE, CINAHL, and PsycINFO), the reference lists of included studies and of individual funding agency websites to identify potential studies for inclusion. To be included, papers had to present an explicit framework or plan either designed for use by researchers or that could be used to guide dissemination activity. Papers which mentioned dissemination (but did not provide any detail) in the context of a wider knowledge translation framework, were excluded. References were screened independently by at least two reviewers; disagreements were resolved by discussion. For each included paper, the source, the date of publication, a description of the main elements of the framework, and whether there was any implicit/explicit reference to theory were extracted. A narrative synthesis was undertaken.

Thirty-three frameworks met our inclusion criteria, 20 of which were designed to be used by researchers to guide their dissemination activities. Twenty-eight included frameworks were underpinned at least in part by one or more of three different theoretical approaches, namely persuasive communication, diffusion of innovations theory, and social marketing.

Conclusions

There are currently a number of theoretically-informed frameworks available to researchers that can be used to help guide their dissemination planning and activity. Given the current emphasis on enhancing the uptake of knowledge about the effects of interventions into routine practice, funders could consider encouraging researchers to adopt a theoretically-informed approach to their research dissemination.

Peer Review reports

Healthcare resources are finite, so it is imperative that the delivery of high-quality healthcare is ensured through the successful implementation of cost-effective health technologies. However, there is growing recognition that the full potential for research evidence to improve practice in healthcare settings, either in relation to clinical practice or to managerial practice and decision making, is not yet realised. Addressing deficiencies in the dissemination and transfer of research-based knowledge to routine clinical practice is high on the policy agenda both in the UK [ 1 – 5 ] and internationally [ 6 ].

As interest in the research to practice gap has increased, so too has the terminology used to describe the approaches employed [ 7 , 8 ]. Diffusion, dissemination, implementation, knowledge transfer, knowledge mobilisation, linkage and exchange, and research into practice are all being used to describe overlapping and interrelated concepts and practices. In this review, we have used the term dissemination, which we view as a key element in the research to practice (knowledge translation) continuum. We define dissemination as a planned process that involves consideration of target audiences and the settings in which research findings are to be received and, where appropriate, communicating and interacting with wider policy and health service audiences in ways that will facilitate research uptake in decision-making processes and practice.

Most applied health research funding agencies expect and demand some commitment or effort on the part of grant holders to disseminate the findings of their research. However, there does appear to be a lack of clarity between funding agencies as to what represents dissemination [ 9 ]. Moreover, although most consider dissemination to be a shared responsibility between those funding and those conducting the research, the expectations on and guidance provided to researchers vary from one agency to another [ 9 ].

We have previously highlighted the need for researchers to consider carefully the costs and benefits of dissemination and have raised concerns about the nature and variation in type of guidance issued by funding bodies to their grant holders and applicants [ 10 ]. Against this background, we have performed a systematic scoping review with the following two aims: to identify and describe any conceptual/organising frameworks designed to be used by researchers to guide their dissemination activities; and to identify and describe any conceptual/organising frameworks relating to knowledge translation continuum that provide enough detail on the dissemination elements that researchers could use it to guide their dissemination activities.

The following databases were searched to identify potential studies for inclusion: MEDLINE and MEDLINE In-Process and Other Non-Indexed Citations (1950 to June 2010); EMBASE (1980 to June 2010); CINAHL (1981 to June 2010); PsycINFO (1806 to June 2010); EconLit (1969 to June 2010); Social Services Abstracts (1979 to June 2010); Social Policy and Practice (1890 to June 2010); Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, Database of Abstracts of Reviews of Effects, Health Technology Assessment Database, NHS Economic Evaluation Database (Cochrane Library 2010: Issue 1).

The search terms were identified through discussion by the research team, by scanning background literature, and by browsing database thesauri. There were no methodological, language, or date restrictions. Details of the database specific search strategies are presented Additional File 1 , Appendix 1.

Citation searches of five articles [ 11 – 15 ] identified prior to the database searches were performed in Science Citation Index (Web of Science), MEDLINE (OvidSP), and Google Scholar (February 2009).

As this review was undertaken as part of a wider project aiming to assess the dissemination activity of UK applied and public health researchers [ 16 ], we searched the websites of 10 major UK funders of health services and public health research. These were the British Heart Foundation, Cancer Research UK, the Chief Scientist Office, the Department of Health Policy Research Programme, the Economic and Social Research Council (ESRC), the Joseph Rowntree Foundation, the Medical Research Council (MRC), the NIHR Health Technology Assessment Programme, the NIHR Service Delivery and Organisation Programme and the Wellcome Trust. We aimed to identify any dissemination/communication frameworks, guides, or plans that were available to grant applicants or holders.

We also interrogated the websites of four key agencies with an established record in the field of dissemination and knowledge transfer. These were the Agency for Healthcare Research and Quality ( AHRQ ) , the Canadian Institutes of Health Research (CIHR), the Canadian Health Services Research Foundation (CHSRF), and the Centre for Reviews and Dissemination (CRD).

As a number of databases and websites were searched, some degree of duplication resulted. In order to manage this issue, the titles and abstracts of records were downloaded and imported into EndNote bibliographic software, and duplicate records removed.

References were screened independently by two reviewers; those studies that did not meet the inclusion criteria were excluded. Where it was not possible to exclude articles based on title and abstract alone, full text versions were obtained and their eligibility was assessed independently by two reviewers. Where disagreements occurred, the opinion of a third reviewer was sought and resolved by discussion and arbitration by a third reviewer.

To be eligible for inclusion, papers needed to either present an explicit framework or plan designed to be used by a researcher to guide their dissemination activity, or an explicit framework or plan that referred to dissemination in the context of a wider knowledge translation framework but that provided enough detail on the dissemination elements that a researcher could then use it. Papers that referred to dissemination in the context of a wider knowledge translation framework, but that did not describe in any detail those process elements relating to dissemination were excluded from the review. A list of excluded papers is included in Additional File 2 , Appendix 2.

For each included paper we recorded the publication date, a description of the main elements of the framework, whether there was any reference to other included studies, and whether there was an explicit theoretical basis to the framework. Included papers that did not make an explicit reference to an underlying theory were re-examined to determine whether any implicit use of theory could be identified. This entailed scrutinising the references and assessing whether any elements from theories identified in other papers were represented in the text. Data from each paper meeting the inclusion criteria were extracted by one researcher and independently checked for accuracy by a second.

A narrative synthesis [ 17 ] of included frameworks was undertaken to present the implicit and explicit theoretical basis of included frameworks and to explore any relationships between them.

Our searches identified 6,813 potentially relevant references (see Figure 1 ). Following review of the titles and abstracts, we retrieved 122 full papers for a more detailed screening. From these, we included 33 frameworks (reported in 44 papers) Publications that did not meet our inclusion criteria are listed in Additional File 2 , Appendix 2.

Identification of conceptual frameworks .

Characteristics of conceptual frameworks designed to be used by researchers

Table 1 summarises in chronological order, twenty conceptual frameworks designed for use by researchers [ 11 , 14 , 15 , 18 – 34 ]. Where we have described elements of frameworks that have been reported across multiple publications, these are referenced in the Table.

Theoretical underpinnings of dissemination frameworks

Thirteen of the twenty included dissemination frameworks were either explicitly or implicitly judged to be based on the Persuasive Communication Matrix [ 35 , 36 ]. Originally derived from a review of the literature of persuasion which sought to operationalise Lasswell's seminal description of persuasive communications as being about 'Who says what in which channel to whom with what effect' [ 37 ]. McGuire argued that there are five variables that influence the impact of persuasive communications. These are the source of communication, the message to be communicated, the channels of communication, the characteristics of the audience (receiver), and the setting (destination) in which the communication is received.

Included frameworks were judged to encompass either three [ 21 , 27 , 29 ], four [ 15 , 20 , 23 , 26 , 28 , 31 , 38 ], or all five [ 11 , 18 , 25 ] of McGuire's five input variables, namely, the source, channel, message, audience, and setting. The earliest conceptual model included in the review explicitly applied McGuire's five input variables to the dissemination of medical technology assessments [ 11 ]. Only one other framework (in its most recent version) explicitly acknowledges McGuire [ 17 ]; the original version acknowledged the influence of Winkler et al . on its approach to conceptualising systematic review dissemination [ 18 ]. The original version of the CRD approach [ 18 , 39 ] is itself referred to by two of the other eight frameworks [ 20 , 23 ]

Diffusion of Innovations theory [ 40 , 41 ] is explicitly cited by eight of the dissemination frameworks [ 11 , 17 , 19 , 22 , 24 , 28 , 29 , 34 ]. Diffusion of Innovations offers a theory of how, why, and at what rate practices or innovations spread through defined populations and social systems. The theory proposes that there are intrinsic characteristics of new ideas or innovations that determine their rate of adoption, and that actual uptake occurs over time via a five-phase innovation-decision process (knowledge, persuasion, decision, implementation, and confirmation). The included frameworks are focussed on the knowledge and persuasion stages of the innovation-decision process.

Two of the included dissemination frameworks make reference to Social Marketing [ 42 ]. One briefly discusses the potential application of social and commercial marketing and advertising principles and strategies in the promotion of non-commercial services, ideas, or research-based knowledge [ 22 ]. The other briefly argues that a social marketing approach could take into account a planning process involving 'consumer' oriented research, objective setting, identification of barriers, strategies, and new formats [ 30 ]. However, this framework itself does not represent a comprehensive application of social marketing theory and principles, and instead highlights five factors that are focussed around formatting evidence-based information so that it is clear and appealing by defined target audiences.

Three other distinct dissemination frameworks were included, two of which are based on literature reviews and researcher experience [ 14 , 32 ]. The first framework takes a novel question-based approach and aims to increase researchers' awareness of the type of context information that might prove useful when disseminating knowledge to target audiences [ 14 ]. The second framework presents a model that can be used to identify barriers and facilitators and to design interventions to aid the transfer and utilization of research knowledge [ 32 ]. The final framework is derived from Two Communities Theory [ 43 ] and proposes pragmatic strategies for communicating across conflicting cultures research and policy; it suggests a shift away from simple one-way communication of research to researchers developing collaborative relationships with policy makers [ 33 ].

Characteristics of conceptual frameworks relating to knowledge translation that could be used by researchers to guide their dissemination activities

Table 2 summarises in chronological order the dissemination elements of 13 conceptual frameworks relating to knowledge translation that could be used by researchers to guide their dissemination activities [ 13 , 44 – 55 ].

Only two of the included knowledge translation frameworks were judged to encompass four of McGuire's five variables for persuasive communications [ 45 , 47 ]. One framework [ 45 ] explicitly attributes these variables as being derived from Winkler et al [ 11 ]. The other [ 47 ] refers to strong direct evidence but does not refer to McGuire or any of the other included frameworks.

Diffusion of Innovations theory [ 40 , 41 ] is explicitly cited in eight of the included knowledge translation frameworks [ 13 , 45 – 49 , 52 , 56 ]. Of these, two represent attempts to operationalise and apply the theory, one in the context of evidence-based decision making and practice [ 13 ], and the other to examine how innovations in organisation and delivery of health services spread and are sustained in health service organisations [ 47 , 57 ]. The other frameworks are exclusively based on the theory and are focussed instead on strategies to accelerate the uptake of evidence-based knowledge and or interventions

Two of the included knowledge translation frameworks [ 50 , 53 ] are explicitly based on resource or knowledge-based Theory of the Firm [ 58 , 59 ]. Both frameworks propose that successful knowledge transfer (or competitive advantage) is determined by the type of knowledge to be transferred as well as by the development and deployment of appropriate skills and infrastructure at an organisational level.

Two of the included knowledge translation frameworks purport to be based upon a range of theoretical perspectives. The Coordinated Implementation model is derived from a range of sources, including theories of social influence on attitude change, the Diffusion of Innovations, adult learning, and social marketing [ 45 ]. The Practical, Robust Implementation and Sustainability Model was developed using concepts from Diffusion of Innovations, social ecology, as well as the health promotion, quality improvement, and implementation literature [ 52 ].

Three other distinct knowledge translation frameworks were included, all of which are based on a combination of literature reviews and researcher experience [ 44 , 51 , 54 ].

Conceptual frameworks provided by UK funders

Of the websites of the 10 UK funders of health services and public health research, only the ESRC made a dissemination framework available to grant applicants or holders (see Table 1 ) [ 26 ]. A summary version of another included framework is available via the publications section of the Joseph Rowntree Foundation [ 60 ]. However, no reference is made to it in the submission guidance they make available to research applicants.

All of the UK funding bodies made brief references to dissemination in their research grant application guides. These would simply ask applicants to briefly indicate how findings arising from the research will be disseminated (often stating that this should be other than via publication in peer-reviewed journals) so as to promote or facilitate take up by users in the health services.

This systematic scoping review presents to our knowledge the most comprehensive overview of conceptual/organising frameworks relating to research dissemination. Thirty-three frameworks met our inclusion criteria, 20 of which were designed to be used by researchers to guide their dissemination activities. Twenty-eight included frameworks that were underpinned at least in part by one or more of three different theoretical approaches, namely persuasive communication, diffusion of innovations theory, and social marketing.

Our search strategy was deliberately broad, and we searched a number of relevant databases and other sources with no language or publication status restrictions, reducing the chance that some relevant studies were excluded from the review and of publication or language bias. However, we restricted our searches to health and social science databases, and it is possible that searches targeting for example the management or marketing literature may have revealed additional frameworks. In addition, this review was undertaken as part of a project assessing UK research dissemination, so our search for frameworks provided by funding agencies was limited to the UK. It is possible that searches of funders operating in other geographical jurisdictions may have identified other studies. We are also aware that the way in which we have defined the process of dissemination and our judgements as to what constitutes sufficient detail may have resulted in some frameworks being excluded that others may have included or vice versa. Given this, and as an aid to transparency, we have included the list of excluded papers as Additional File 2 , Appendix 2 so as to allow readers to assess our, and make their own, judgements on the literature identified.

Despite these potential limitations, in this review we have identified 33 frameworks that are available and could be used to help guide dissemination planning and activity. By way of contrast, a recent systematic review of the knowledge transfer and exchange literature (with broader aims and scope) [ 61 ] identified five organising frameworks developed to guide knowledge transfer and exchange initiatives (defined as involving more than one way communications and involving genuine interaction between researchers and target audiences) [ 13 – 15 , 62 , 63 ]. All were identified by our searches, but only three met our specific inclusion criteria of providing sufficient dissemination process detail [ 13 – 15 ]. One reviewed methods for assessment of research utilisation in policy making [ 62 ], whilst the other reviewed knowledge mapping as a tool for understanding the many knowledge creation and translation resources and processes in a health system [ 63 ].

There is a large amount of theoretical convergence among the identified frameworks. This all the more striking given the wide range of theoretical approaches that could be applied in the context of research dissemination [ 64 ], and the relative lack of cross-referencing between the included frameworks. Three distinct but interlinked theories appear to underpin (at least in part) 28 of the included frameworks. There has been some criticism of health communications that are overly reliant on linear messenger-receiver models and do not draw upon other aspects of communication theory [ 65 ]. Although researcher focused, the included frameworks appear more participatory than simple messenger-receiver models, and there is recognition of the importance of context and emphasis on the key to successful dissemination being dependent on the need for interaction with the end user.

As we highlight in the introduction, there is recognition among international funders both of the importance of and their role in the dissemination of research [ 9 ]. Given the current political emphasis on reducing deficiencies in the uptake of knowledge about the effects of interventions into routine practice, funders could be making and advocating more systematic use of conceptual frameworks in the planning of research dissemination.

Rather than asking applicants to briefly indicate how findings arising from their proposed research will be disseminated (as seems to be the case in the UK), funding agencies could consider encouraging grant applicants to adopt a theoretically-informed approach to their research dissemination. Such an approach could be made a conditional part of any grant application process; an organising framework such as those described in this review could be used to demonstrate the rationale and understanding underpinning their proposed plans for dissemination. More systematic use of conceptual frameworks would then provide opportunities to evaluate across a range of study designs whether utilising any of the identified frameworks to guide research dissemination does in fact enhance the uptake of research findings in policy and practice.

There are currently a number of theoretically-informed frameworks available to researchers that could be used to help guide their dissemination planning and activity. Given the current emphasis on enhancing the uptake of knowledge about the effects of interventions into routine practice, funders could consider encouraging researchers to adopt a theoretically informed approach to their research dissemination.

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Acknowledgements

This review was undertaken as part of a wider project funded by the MRC Population Health Sciences Research Network (Ref: PHSRN 11). The views expressed in this paper are those of the authors alone.

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Wilson, P.M., Petticrew, M., Calnan, M.W. et al. Disseminating research findings: what should researchers do? A systematic scoping review of conceptual frameworks. Implementation Sci 5 , 91 (2010). https://doi.org/10.1186/1748-5908-5-91

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What does communicating results mean?

Over the last 20 years the understanding of what communicating market research results means has changed. It used to be something that described what the researcher delivered. Today, the focus is on what is understood and what actions result from it.

The old view of communicating results Communicating market research results used to mean producing something that:

• Was clearly written
• Had all the relevant information
• Had clear recommendations

Communicating was viewed as an output. Indeed market researchers often used the term ‘deliverables’ to describe this output. When the communication was delivered, the job was done.

However, that has changed.

Communication is about what is heard, not what is said! Consider the cartoon below.

The researcher in the cartoon might feel she had communicated the findings quite clearly. However, the reaction to the report was not the outcome the researcher hoped for – which means the communication failed.

Evaluating communication Communication must be evaluated in terms of the message that has been received by the recipient, and ideally by the actions the recipient then takes.

In the lottery example above, the minimum goal for the researcher is for the client to understand that the research says playing the lottery is foolish. The ideal outcome is that the client understands that playing the lottery is foolish and so decides to stop playing it.

It still has to be based on research! An engaging story from a market researcher, that communicates an idea, and that leads to action has to be based on evidence. The researcher’s evidence will typically have to be made available, so that the story could be checked and so that other narratives might be derived from it.

Focusing on the impact of the communication does not remove any of the rules of good research, or good presenting, or good report writing. Focusing on the impact is an additional and essential step in how to communicate research results.

One thought on “ What does communicating results mean? ”

I am confused. So what do Communicate results mean today?

Comments are closed.

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Administrative data

Administrative data is the term used to describe everyday data about individuals collected by government departments and agencies. Examples include exam results, benefit receipt and National Insurance payments.

Age effects

Age effects relates to changes in an outcome as a result of getting older.

Anonymisation

Anonymisation refers to the removal of study participants ’ identifying information (e.g., name, address) in order to preserve their privacy.

Attrition is the discontinued participation of study participants in a longitudinal study. Attrition can reflect a range of factors, from the study participant not being traceable to them choosing not to take part when contacted. Attrition is problematic both because it can lead to bias in the study findings (if the attrition is higher among some groups than others) and because it reduces the size of the sample .

Baseline refers to the start of a study when initial information is collected on participation (however, in longitudinal studies , researchers may adopt an alternative ‘baseline’ for the purposes of analysis).

Biological samples

Biological samples is the term used for specimens collected from human subjects from which biological information, such as genetic markers, can be extracted for analysis. Common examples include blood, saliva or hair.

Body mass index

Body mass index is a measure used to assess if an individual is a healthy weight for their height. It is calculated by dividing the individual’s weight by the square of their height, and it is typically represented in units of kg/m 2 .

Boosted samples

Boosted samples are used to overcome sample bias due to attrition or to supplement the representation of smaller sub-groups within the sample . Inclusion of boosted samples must be accompanied by appropriate survey weights .

Computer-assisted personal interviewing (CAPI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a face-to-face interview.

Computer-assisted self-interviewing (CASI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a self-completion questionnaire.

Categorical variable

A categorical variable is a variable that can take one of a limited number of discrete values. They can be either nominal – they contain no inherent order of categories (e.g. sex; marital status) – or ordinal – they can be ranked in some meaningful order (e.g. level of satisfaction with a service).

Computer-assisted telephone interviewing (CATI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a telephone interview.

For some study participants the exact time of an event will not be known because either: the study ends (or the analysis is carried out) before they have had the event, or the participant drops out of the study before experiencing the event. It is therefore, only known that the event has not occurred up to the time that they were last observed in the study.

Census refers to a universal and systematic collection of data from all individuals within a population . In the UK, the government conducts a census every ten years with the next one due in 2021.

A codebook is a document (online or hard-copy) that contains all the information about how a dataset has been coded, such that it can be deciphered by a researcher not familiar with the original coding frame.

Coding is the process of converting survey responses into numerical codes to facilitate data analysis. All potential responses (as well as possible reasons for non-response) for each variable are assigned numerical values according to a coding frame.

Cognitive assessments

Cognitive assessments are exercises used to measure thinking abilities, such as memory, reasoning and language. Longitudinal studies collecting data in this way can track the extent to which someone’s cognitive abilities change (develop or decline) over time.

Cohort studies

Cohort studies are concerned with charting the lives of groups of individuals who experience the same life events within a given time period. The best known examples are birth cohort studies, which follow a group of people born in a particular period.

Complete case analysis

Complete case analysis is the term used to describe a statistical analysis that only includes participants for which we have no missing data on the variables of interest. Participants with any missing data are excluded.

Conditioning

Conditioning refers to the process whereby participants’ answers to some questions may be influenced by their participation in the study – in other words, their responses are ‘conditioned’ by their being members of a longitudinal study. Examples would include study respondents answering questions differently or even behaving differently as a result of their participation in the study.

Confounding

Confounding occurs where the relationship between independent and dependent variables is distorted by one or more additional, and sometimes unmeasured, variables . A confounding variable must be associated with both the independent and dependent variables but must not be an intermediate step in the relationship between the two (i.e. not on the causal pathway).

For example, we know that physical exercise (an independent variable) can reduce a person’s risk of cardiovascular disease (a dependent variable ). We can say that age is a confounder of that relationship as it is associated with, but not caused by, physical activity and is also associated with coronary health. See also ‘ unobserved heterogeneity ’, below.

Continuous variable

A continuous variable is a variable that has an infinite number of uncountable values e.g. time, temperature. They are also known as quantitative variables or scale variables .

Cohort effects

Cohort effects relates to changes in an outcome associated with being a member of a specific cohort of people (e.g. born in the same year; or starting school at the same time).

In metadata management, coverage refers to the temporal, spatial and topical aspects of the data collection to describe the comprehensiveness of a dataset. For longitudinal studies , this can relate to the topics that are covered across waves, the population to which one can generalise or the geographic extent of the dataset.

Cross-sectional

Cross-sectional surveys involve interviewing a fresh sample of people each time they are carried out. Some cross-sectional studies are repeated regularly and can include a large number of repeat questions (questions asked on each survey round).

Data access agreement

Within the context of data protection , a data access agreement specifies the terms under which users are provided access to specified datasets. This usually forms part of the application process to the data controller to ensure that researchers adhere to a set of terms regarding data confidentiality , sensitivity and dissemination before accessing the data. See also: research ethics

Data cleaning

Data cleaning is an important preliminary step in the data analysis process and involves preparing a dataset so that it can be correctly analysed. ‘Cleaning’ the data usually involves identifying data input errors, assessing the completeness of the dataset and verifying any anomalies (e.g. outliers).

Data confidentiality

Within the context of data protection , data confidentiality is the process of protecting participants’ data from being accessed or disclosed by those unauthorised to do so. Key methods employed in data confidentiality include anonymisation of responses (removal of personal identifying information) and data encryption (protecting the data using codes and/or passwords).

Data harmonisation

Data harmonisation involves retrospectively adjusting data collected by different surveys to make it possible to compare the data that was collected. This enables researchers to make comparisons both within and across studies. Repeating the same longitudinal analysis across a number of studies allows researchers to test whether results are consistent across studies, or differ in response to changing social conditions.

Data imputation

Data imputation is a technique for replacing missing data with an alternative estimate. There are a number of different approaches, including mean substitution and model-based multivariate approaches.

Data linkage

Data linkage simply means connecting two or more sources of administrative, educational, geographic, health or survey data relating to the same individual for research and statistical purposes. For example, linking housing or income data to exam results data could be used to investigate the impact of socioeconomic factors on educational outcomes.

Data protection

Data protection refers to the broad suite of rules governing the handling and access of information about people. Data protection principles include confidentiality of responses, informed consent of participants and security of data access. These principles are legally protected by the Data Protection Act (DPA) and the General Data Protection Regulation (GDPR).

Data structure

Data structure refers to the way in which data are organised and formatting in advance of data analysis.

Dependent variable

In analysis, the dependent variable is the variable you expect to change in response to different values of your independent (or predictor) variables . For example, a students’ test results may be (partially) explained by the number of hours spent on revision. In this case, the dependent variable is students’ test score, which you expect to be different according to the amount of time spent revising.

Derived variable

A derived variable is a variable that is calculated from the values of other variables and not asked directly of the participants. It can involve a mathematical calculation (e.g. deriving monthly income from annual income by dividing by 12) or a recategorisation of one or more existing variables (e.g. categorising monthly income into £500 bands – £0 to £500, £501 to £1,000, etc.)

Diaries are a data collection instrument that is particularly useful in recording information about time use or other regular activity, such as food intake. They have the benefit of collecting data from participants as and when an activity occurs. As such, they can minimise recall bias and provide a more accurate record of activities than a retrospective interview.

Dissemination

Dissemination is the process of sharing information – particularly research findings – to other researchers, stakeholders, policy makers, and practitioners through various avenues and channels, including online, written publications and events. Dissemination is a planned process that involves consideration of target audiences in ways that will facilitate research uptake in decision-making processes and practice.

Dummy variables

Dummy variables , also called indicator variables , are sets of dichotomous (two-category) variables we create to enable subgroup comparisons when we are analysing a categorical variable with three or more categories.

Empirical data

Empirical data refers to data collected through observation or experimentation. Analysis of empirical data can provide evidence for how a theory or assumption works in practice.

In metadata management, fields are the elements of a database which describes the attributes of items of data.

General ability

General ability is a term used to describe cognitive ability, and is sometimes used as a proxy for intelligent quotient (IQ) scores.

Growth curve modelling

Growth curve modelling is used to analyse trajectories of longitudinal change over time allowing us to model the way participants change over time, and then to explore what characteristics or circumstances influence these patterns of longitudinal change.

Hazard rate

Hazard rate refers to the probability that an event of interest occurs at a given time point, given that it has not occurred before.

Health assessments

Health assessments refers to the assessments carried out on research participants in relation to their physical characteristics or function. These can include measurements of height and weight, blood pressure or lung function.

Heterogeneity

Heterogeneity is a term that refers to differences, most commonly differences in characteristics between study participants or samples. It is the opposite of homogeneity, which is the term used when participants share the same characteristics. Where there are differences between study designs, this is sometimes referred to as methodological heterogeneity. Both participant or methodological differences can cause divergences between the findings of individual studies and if these are greater than chance alone, we call this statistical heterogeneity. See also: unobserved heterogeneity .

Household panel surveys

Household panel surveys collect information about the whole household at each wave of data collection, to allow individuals to be viewed in the context of their overall household. To remain representative of the population of households as a whole, studies will typically have rules governing how new entrants to the household are added to the study.

Incentives and rewards

As a way of encouraging participants to take part in research, they may be offered an incentive or a reward. These may be monetary or, more commonly, non-monetary vouchers or tokens. Incentives are advertised beforehand and can act as an aid to recruitment; rewards are a token of gratitude to the participants for giving their time.

Independent variable

In analysis, an independent variable is any factor that may be associated with an outcome or dependent variable . For example, the number of hours a student spends on revision may influence their test result. In this case, the independent variable, revision time (at least partially) ‘explains’ the outcome of the test.

Informed consent

A key principle of research ethics , informed consent refers to the process of providing full details of the research to participants so that they are sufficiently able to choose whether or not to consent to taking part.

Kurtosis is sometimes described as a measure of ‘tailedness’. It is a characteristic of the distribution of observations on a variable and denotes the heaviness of the distribution’s tails. To put it another way, it is a measure of how thin or fat the lower and upper ends of a distribution are.

Life course

A person’s life course refers to the experiences and stages an individual passes through during their life. It centres on the individual and emphasises the changing social and contextual processes that influence their life over time.

Longitudinal studies

Longitudinal studies gather data about the same individuals (‘ study participants ’) repeatedly over a period of time, in some cases from birth until old age. Many longitudinal studies focus upon individuals, but some look at whole households or organisations.

Metadata refers to data about data, which provides the contextual information that allows you to interpret what data mean.

Missing data

Missing data refers to values that are missing and do not appear in a dataset. This may be due to item non-response, participant drop-out (or attrition ) or, in longitudinal studies , some data (e.g. date of birth) may be collected only in some waves. Large amounts of missing data can be a problem and lead researchers to make erroneous inferences from their analysis. There are several ways to deal with the issue of missing data, from casewise deletion to complex multiple imputation models.

Multi-level modelling

Multi-level modelling refers to statistical techniques used to analyse data that is structured in a hierarchical or nested way. For example. study participants who belong to the same household, or students who attend the same school may be expected to be more similar to each other than to participants in other households or schools (such as sharing similar contextual influences). This similarity means that the data from participants within these households/schools are not independent. Multi-level models can account for variability at both the individual level and the group (e.g. household or school) level.

Non-response bias

Non-response bias is a type of bias introduced when those who participate in a study differ to those who do not in a way that is not random (for example, if attrition rates are particularly high among certain sub-groups). Non-random attrition over time can mean that the sample no longer remains representative of the original population being studied. Two approaches are typically adopted to deal with this type of missing data : weighting survey responses to re-balance the sample , and imputing values for the missing information.

Observational studies

Observational studies focus on observing the characteristics of a particular sample without attempting to influence any aspects of the participants’ lives. They can be contrasted with experimental studies, which apply a specific ‘treatment’ to some participants in order to understand its effect.

Panel studies

Panel studies follow the same individuals over time. They vary considerably in scope and scale . Examples include online opinion panels and short-term studies whereby people are followed up once or twice after an initial interview.

Peer review

Peer review is a method of quality control in the process of academic publishing, whereby research is appraised (usually anonymously) by one or more independent academic with expertise in the subject.

Period effects

Period effects relate to changes in an outcome associated with living during a particular time, regardless of age or cohort membership (e.g. living through times of war, economic recession or global pandemic).

Piloting is the process of testing your research instruments and procedures to identify potential problems or issues before implementing them in the full study. A pilot study is usually conducted on a small subset of eligible participants who are encouraged to provide feedback on the length, comprehensibility and format of the process and to highlight any other potential issues.

Population refers to all the people of interest to the study and to whom the findings will be able to be generalized (e.g. a study looking into rates of recidivism may have a [target] population encompassing everyone with a criminal conviction). Owing to the size of the population, a study will usually select a sample from which to make inferences. See also: sample , representiveness.

Percentiles

A percentile is a measure that allows us to explore the distribution of data on a variable. It denotes the percentage of individuals or observations that fall below a specified value on a variable. The value that splits the number of observations evenly, i.e. 50% of the observations on a variable fall below this value and 50% above, is called the 50th percentile or more commonly, the median.

Primary research

Primary research refers to original research undertaken by researchers collecting new data. It has the benefit that researchers can design the study to answer specific questions and hypotheses rather than relying on data collected for similar but not necessarily identical purposes. See also: secondary research

Prospective study

In prospective studies, individuals are followed over time and data about them is collected as their characteristics or circumstances change.

Qualitative data

Qualitative data are non-numeric – typically textual, audio or visual. Qualitative data are collected through interviews, focus groups or participant observation. Qualitative data are often analysed thematically to identify patterns of behaviour and attitudes that may be highly context-specific.

Quantitative data

Quantitative data can be counted, measured and expressed numerically. They are collected through measurement or by administering structured questionnaires . Quantitative data can be analysed using statistical techniques to test hypotheses and make inferences to a population .

Questionnaires

Questionnaires are research instruments used to elicit information from participants in a structured way. They might be administered by an interviewer (either face-to-face or over the phone), or completed by the participants on their own (either online or using a paper questionnaire. Questions can cover a wide range of topics and often include previously-validated instruments and scales (e.g. the Rosenberg Self-Esteem Scale ).

Recall error or bias

Recall error or bias describes the errors that can occur when study participants are asked to recall events or experiences from the past. It can take a number of forms – participants might completely forget something happened, or misremember aspects of it, such as when it happened, how long it lasted, or other details. Certain questions are more susceptible to recall bias than others. For example, it is usually easy for a person to accurately recall the date they got married, but it is much harder to accurately recall how much they earned in a particular job, or how their mood at a particular time.

Record linkage

Record linkage studies involve linking together administrative records (for example, benefit receipts or census records) for the same individuals over time.

Reference group

A reference group is a category on a categorical variable to which we compare other values. It is a term that is commonly used in the context of regression analyses in which categorical variables are being modelled.

Regression analysis

Regression analysis refers to analytical techniques that use a mathematical ‘model’ to predict values of a dependent variable from values of one or many independent variable (s).

Repeated measures

Repeated measures are measurements of the same variable at multiple time points on the same participants, allowing researchers to study change over time.

Representativeness

Representativeness is the extent to which a sample is representative of the population from which it is selected. Representative samples can be achieved through, for example, random sampling, systematic sampling, stratified sampling or cluster sampling.

Research ethics

Research ethics relates to the fundamental codes of practice associated with conducting research. Ethical issues that need to be considered include providing informed consent to participants, non-disclosure of sensitive information, confidentiality and anonymity safeguarding of vulnerable groups , and respect for participants’ well-being. Academic research proposals need be approved by an ethics committee before any actual research (either primary or secondary) can begin.

Research impact

Research impact is the demonstrable contribution that research makes to society and the economy that can be realised through engagement with other researchers and academics, policy makers, stakeholders and members of the general public. It includes influencing policy development, improving practice or service provision, or advancing skills and techniques.

Residuals are the difference between your observed values (the constant and predictors in the model) and expected values (the error), i.e. the distance of the actual value from the estimated value on the regression line.

Respondent burden

Respondent burden is a catch all phrase that describes the perceived burden faced by participants as a result of their being involved in a study. It could include time spent taking part in the interview and inconvenience this may cause, as well as any difficulties faced as a result of the content of the interview.

Response rate

Response rate refers to the proportion of participants in the target sample who completed the survey. Longitudinal surveys are designed with the expectation that response rates will decline over time so will typically seek to recruit a large initial sample in order to compensate for likely attrition of participants.

Retrospective study

In retrospective studies, individuals are sampled and information is collected about their past. This might be through interviews in which participants are asked to recall important events, or by identifying relevant administrative data to fill in information on past events and circumstances.

Sample is a subset of a population that is used to represent the population as a whole. This reflects the fact that it is often not practical or necessary to survey every member of a particular population . In the case of birth cohort studies , the larger ‘ population ’ from which the sample is drawn comprises those born in a particular period. In the case of a household panel study like Understanding Society, the larger population from which the sample was drawn comprised all residential addresses in the UK.

Sample size

Sample size refers to the number of data units contained within a dataset. It most frequently refers to the number of respondents who took part in your study and for whom there is usable data. However, it could also relate to households, countries or other institutions. The size of a sample , relative to the size of the population , will have consequences for analysis: the larger a sample is, the smaller the margin of error of its estimates, the more reliable the results of the analysis and the greater statistical power of the study.

Sampling frame

A sampling frame is a list of the target population from which potential study participants can be selected.

Scales are frequently used as part of a research instrument seeking to measure specific concepts in a uniform and replicable way. Typically, they are composed of multiple items that are aggregated into one or more composite scores. Examples of standardised scales include the British Ability Scale (BAS); the Malaise Inventory; and the Rosenberg Self-Esteem Scale.

Scatterplot

A scatterplot is a way of visualising the relationship between two continuous variables by plotting the value of each associated with a single case on a set of X-Y coordinates. For example, students’ test scores in English and maths can be represented as point on a graph, with each point representing a single student’s English (x-axis) and maths (y-axis) score. Looking at data for many students allows us to build up a visualisation of the relationship between students’ scores in maths and English.

Secondary research

Secondary research refers to new research undertaken using data previously collected by others. It has the benefit of being more cost-effective than primary research whilst still providing important insights into research questions under investigation.

Skewness is the measure of how assymetrical the distribution of observations are on a variable. If the distribution has a more pronounced/longer tail at the upper end of the distribution (right-hand side), we say that the distribution is negatively skewed. If it is more pronounced/longer at the lower end (left-hand side), we say that it is positively skewed.

Statistical model

A statistical model is a mathematical representation of the relationship between variables .

Statistical software

Statistical software packages are specifically designed to carry out statistical analysis; these can either be open-source (e.g. R ) or available through institutional or individual subscription (e.g. SPSS ; Stata ).

Structured metadata

Structured metadata define the relationship between data items/objects to enable computer systems to understand the contextual meaning of the data. It uses standardised content to facilitate the use of metadata for data discovery and sharing, and the relationship between metadata elements.

Study participants

Study participants are the individuals who are interviewed as part of a longitudinal study.

Survey logic

Also called conditional routing (sometimes called ‘filters’), survey logic refers to the flow that takes respondents through a survey. Respondents may be required to answer some questions only if they had provided a relevant response to a previous question. E.g. Only respondents who are currently at university may be asked to answer a question relating to their degree subject. This is important when considering missing data .

Survey weights

Survey weights can be used to adjust a survey sample so it is representative of the survey population as a whole. They may be used to reduce the impact of attrition on the sample , or to correct for certain groups being over-sampled.

Survival analysis

Survival analysis is an analytical technique that uses time-to-event data to statistically model the probability of experiencing an event by a given time point. For example, time to retirement, disease onset or length of periods of unemployment.

The term used to refer to a round of data collection in a particular longitudinal study (for example, the age 7 sweep of the National Child Development Study refers to the data collection that took place in 1965 when the participants were aged 7). Note that the term wave often has the same meaning.

Target population

The population of people that the study team wants to research, and from which a sample will be drawn.

Time to event

Time to event refers to the duration of time (e.g. in hours, days, months, etc.) from a defined baseline to the time of occurrence of an event of interest (e.g. diagnosis of an illness, first re-offence following release from prison). Survival analysis can be used to analyse such data.

Tracing (or tracking)

Tracing (or tracking) describes the process by which study teams attempt to locate participants who have moved from the address at which they were last interviewed.

Unobserved heterogeneity

Unobserved heterogeneity is a term that describes the existence of unmeasured (unobserved) differences between study participants or samples that are associated with the (observed) variables of interest. The existence of unobserved variables means that statistical findings based on the observed data may be incorrect.

Part of the documentation that is usually provided with statistical datasets, user guides are an invaluable resource for researchers. The guides contain information about the study, including the sample , data collection procedures, and data processing. Use guides may also provide information about how to analyse the data, whether there are missing data due to survey logic , and advice on how to analyse the data such the application of survey weights .

Variables is the term that tends to be used to describe data items within a dataset. So, for example, a questionnaire might collect information about a participant’s job (its title, whether it involves any supervision, the type of organisation they work for and so on). This information would then be coded using a code-frame and the results made available in the dataset in the form of a variable about occupation. In data analysis variables can be described as ‘dependent’ and ‘independent’, with the dependent variable being a particular outcome of interest (for example, high attainment at school) and the independent variables being the variables that might have a bearing on this outcome (for example, parental education, gender and so on).

Vulnerable groups

Vulnerable groups refers to research participants who may be particularly susceptible to risk or harm as a result of the research process. Different groups might be considered vulnerable in different settings. The term can encompass children and minors, adults with learning difficulties, refugees, the elderly and infirm, economically disadvantaged people, or those in institutional care. Additional consideration and mitigation of potential risk is usually required before research is carried out with vulnerable groups.

The term used to refer to a round of data collection in a particular longitudinal study (for example, the age 7 wave of the National Child Development Study refers to the data collection that took place in 1965 when the participants were aged 7). Note that the term sweep often has the same meaning.

Learning Hub

Why communicate your research.

Communicating the findings of your research is a key part of an academic endeavour. This can involve publishing your work for the benefit of the academic community, sharing findings with interested parties or policy and decision makers, or disseminating them to the general public.

As an undergraduate, your research may only be written up for submission to a supervisor or assessor, yet postgraduates and academic staff habitually engage in disseminating their research findings through conferences, journal articles and books. It is important, therefore, to develop the skills associated with the effective communication of complex ideas as you develop other research skills. Disseminating research is a key phase in the research cycle.

Source: Bournemouth University (2015). Research Lifecycle. BU Research Blog.

Research communication encompasses a wide range of skills, including written communication (journal articles, book chapters, policy briefs, reports, blog posts); oral communication (conference presentations, workshops, lectures, events) and visual communication (conference posters, presentation slides, videos).

Understanding how your research can contribute to wider society is an important part of communicating your findings to a range of audiences. The benefits – or impact – of your research can be realised through engagement with other researchers and academics, policy and decision makers, stakeholders and members of the general public. Engagement in this sense encompasses all activities that seek to share the potential benefits of research with a wider audience.

It is by definition a two-way process, involving interaction and listening, with the goal of generating mutual benefit . The presentation of your research findings provides the opportunity to elicit feedback from stakeholders and experts and others with a professional or academic interest in the subject. This, in turn, can feed back into your future research and help develop networks for future collaboration and cooperation.

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Dissemination or participation? Exploring scientists’ definitions and science communication goals in the Netherlands

Adina nerghes.

1 Strategic Communication, Department of Social Sciences, Wageningen University & Research, Wageningen, The Netherlands

2 Philosophy, Department of Social Sciences, Wageningen University & Research, Wageningen, The Netherlands

Ju-Sung Lee

3 Erasmus School of History, Culture and Communication, Department of Media and Communication, Erasmus University Rotterdam, Rotterdam, The Netherlands

Associated Data

The data underlying the results presented in the study are publicly available from the DANS repository ( https://doi.org/10.17026/dans-zrs-fx7c ).

The field of science communication has grown considerably over the past decade, and so have the number of scientific writings on what science communication is and how it should be practiced. The multitude of theoretisations and models has led to a lack of clarity in defining science communication, and to a highly popularised—and theorised—rhetorical shift from deficit to dialogue and participation. With this study, we aim to remediate the absence of research into what science communication is, for scientists themselves. We also investigate whether the transition towards dialogue and participation is reflected in the goals scientists identify as important to their science communication efforts, both in a general and a social media context. For this, we analyse survey data collected from scientists in the Netherlands using thematic qualitative analysis and statistical analysis. Our results reveal six main dimensions of science communication as defined by our respondents. The 584 definitions we analyse demonstrate a focus on a one-way process of transmission and translation of scientific results and their impacts towards a lay audience, via mostly traditional media channels, with the goals of making science more accessible, of educating audiences, and of raising awareness about science. In terms of the goals identified as most important by scientists in the Netherlands, we find goals aligned with the deficit and dialogue models of science communication to be the most important. Overall, our findings suggest we should be cautious in the face of recent claims that we live in a new era of dialogue, transparency, and participation in the realm of science communication.

Introduction

In recent years, science communication has received increasing attention from politics and society, and it has developed into a vital part of academic activity [ 1 ]. As science communication is of rising importance for scientists, and even for their academic careers [ 2 ], our work aims to bring a timely and important contribution to this area of research. We aim to contribute towards a broader understanding and deeper insights into science communication from the perspective of scientists in terms of individual viewpoints and the goals scientists focus on in their science communication practices as well as the goals scientists are trying to achieve when using social media for science communication.

As a growing area of practice and research, science communication over the last 50 years has developed into a field primarily concerned with answering questions related to science and society, science in the media, and the role of science journalists [ 3 ]. In the academic realm, specifically, these questions are addressed by researchers of sociology, journalism, and media studies. As it is the case with most multidisciplinary, rich research fields, science communication has produced different—and sometimes even contradictory—theorisations [ 4 ]. It comes as no surprise, then, that scholars have not agreed on a clear and shared definition of science communication [ 4 , 5 ].

In very general terms, science communication refers to communication on scientific topics and the relationship between science and society. But as Burns, O’Connor and Stocklmayer state [ 6 ], the definition of science communication has been “plagued by an unfortunate lack of clarity” (p.183). The wide array of definitions offered by the science communication literature ranges from those referencing some set of signature methods and aims [ 7 ] to those focusing on “manifest or latent purposes of science communication practice” [ 4 , p.4].

For example, Davies [ 8 ] defines science communication in a contemporary conception as “organized processes that seek to engage lay publics with scientific knowledge.” (p.163) While this definition seems simple and straightforward, it restricts science communication to those processes in which the public engages with scientific knowledge as a finished product. Such definitions are in clear contrast to those highlighting the importance of public engagement in the process of generating scientific knowledge [ 9 , 10 ] and those advocating for a change in perspective—from a communication process to a ‘conversation’ [ 4 ]. Bryant [ 11 ] takes further steps to acknowledge the intangible cultural aspects of science communication and that science communication is a continual process rather than a one-off, linear activity and defines science communication as “… the processes by which the culture and knowledge of science are absorbed into the culture of the wider community.”

Scholars such as Burns [ 6 ] take a more pragmatic approach, formulating a definition aimed at providing the framework for evaluating the effectiveness of science communication from an outcomes oriented perspective:

“Science communication […] is defined as the use of appropriate skills, media, activities, and dialogue to produce one or more of the following personal responses to science (the AEIOU vowel analogy): Awareness, Enjoyment, Interest, Opinion-forming, and Understanding.”

The definitions exemplified above are by no means the only ones nor do they include all involved actors, beyond scientists and the lay public (such as institutions, journalists, etc.) and all societal contexts (e.g., policy-making). However, an exhaustive review of all the science communication definitions is beyond the scope of this article. Rather than attempt to further portray the breadth of science communication definitions, we would like to highlight the importance of exploring scientists’ personal science communication definitions. While some studies have attempted to understand scientists’ choices and objectives using the Theory of Planned Behavior [ 12 ] and the associated Integrated Behavioral Model [ 13 ], and others have focused on understanding scientists’ views about specific communication objectives and strategies [ 14 ], our contribution focuses on uncovering how scientists themselves define science communication.

A survey conducted in the UK by Illingworth and colleagues [ 15 ] further highlights the importance of understanding scientists’ personal definitions of science communication and how such definitions may be more nuanced and expansive than those presented by literature or various organisations. They show that, even though scientists believed their personal definitions were aligned with pre-existing ones, in reality the very wide range of personal definitions was more nuanced and expansive [ 15 ]. Thus, empirically assessing various aspects of science communication from the perspective of a pre-determined definition creates an unnecessarily rigid analysis framework.

Empirical assessment departing from the scientists’ personal science communication definitions can foster a broader understanding and deeper insights [ 16 , 17 ] into individual viewpoints [ 18 , 19 ]. By focusing on scientists’ “logic and the kinds of evidence they considered worthwhile and relevant, we suspend our own notions of what is right.” [ 20 ] In other words, the investigation of personal definitions can help us identify and understand what science communication is from the perspective of those who are closest to the scene. Here we argue that—while understudied—scientists’ personal definitions form the basis for their approaches to science communication and their communication activities. The importance of understanding scientists’ “individual conceptions of science communication” (or what the authors refer to as ‘mental models’) is further highlighted by a very recent study conducted by Kessler and colleagues across three European countries [ 21 ]. Their findings suggest that scientists’ individual conceptions largely align with the ways in which they practice science communication.

The scientific community has often been primarily focused on disseminating information and building public knowledge about science [ 22 – 24 ] and on developing theoretical models that describe the relationship between scientists and the public [ 25 , 26 ]. However, very few studies have focused on understanding the specific goals scientists are trying to accomplish through their science communication efforts [ 21 , 27 ], and scholars have generally not treated communication choices related to strategies, objectives, and goals as behaviours that can both be studied and reshaped [ 28 ].

Over the last decades, a rhetorical shift has taken place in the area of science communication in that calls for dialogue between scientists and non-scientists, as well as calls for more participatory approaches to science communication, have taken precedence over the scientific literacy deficits rhetoric. Together with this rhetorical shift from ‘deficit’ towards ‘dialogue’ and ‘participation’, scholars have developed a number of science communication models ranging from transmission of information to dialogue and public engagement. Such models are said to be frameworks for understanding what the “problem” is, how to measure the problem, and how to address the problem [ 29 , p.13]. Models of science communication, emerging in the literature, represent how theorists believe that science has been, is being, or should be communicated [ 30 ]. However, as we will argue, the assumptions behind most of these models have not been comprehensively tested against real-life practices of science communication [ 31 ]. But let us first discuss three of the most prominent models, their underlining assumptions, and described goals.

Historically, science communication had been described as a process of information transmission, which assumed “public deficiency, but scientific sufficiency” [ 32 ]. What has become known as the deficit model of science communication was first discussed in the literature during the 1980s and early 1990s [ 33 , 34 ]. This model asserts that science communication is guided by a perceived need for science literacy, where the public is viewed as having a deficit of scientific knowledge until such knowledge is received from the communicator [ 35 – 37 ]. As Irwin [ 38 ] describes, the deficit model aims at ‘public understanding of science’ and is characterised as a one-way, top-down communication process. In other words, science communication is a one-way process from the scientists (‘experts’) to the public (‘non-experts’) in which scientists—with all the required information—filled the knowledge vacuum in the scientifically illiterate general public as they see fit [ 39 ].

The perspective of the deficit model is perfectly exemplified by the UK Chief Scientific Adviser to the House of Lords, in a report published in 2000 which purports “difficulties in the relationship between science and society are due entirely to ignorance on the part of the public” and “with enough public-understanding activity, the public can be brought to greater knowledge, whereupon all will be well.” [ 40 , p.25]. This assumption that by fixing the deficit everything will be ‘well’ has led to the emergence of many large-scale projects focused on science literacy, often addressing revisions of national educational curricula [ 41 ]. However, research has shown not only that the deficit assumption is an incorrect one—as people with limited education can quickly come to understand highly complex technical information [ 42 ]—but that after more than 25 years of ‘filling the deficit,’ the public’s ‘knowledge’ seems unaffected and remarkably stable [ 43 ]. Under the deficit perspective, by making the knowledge deficits of the public central to the problem of science communication, many scientists ignore the possibility that their communication approaches might be part of the problem [ 44 ].

Scholars have theorised that the condescending claims of the public’s ignorance under the deficit model [ 45 ] and the increasing signs of public unease with science during the 1980s [ 30 ] have led to a new style of science communication as embodied in the dialogue model. This new model of science communication draws on the belief that “more active, open and democratic relations between science and citizens are both desirable and necessary.” [ 38 , p.200]

The dialogue model is characterised by three main features: (1) engagement in a dialogue with the public to help explain the science [ 46 ], (2) listening to and consulting the public about their perceptions, concerns and needs with regard to science [ 37 , 47 ], and (3) acknowledging that the public may have useful knowledge that can help scientific progress and policy-making [ 48 ]. Under this model, science communication is centred around dialogic approaches that aim for a two-way conversation, allowing an exchange of perspectives between scientists and the public.

The emergence of the dialogue model, together with the increasing demand for scientists’ involvement in public discussions and with policy documents shifting their language from ‘communication’ to ‘dialogue,’ have created a powerful narrative in which this type of science communication is portrayed as inherently superior [ 49 ]. However, many scholars have argued that this shift from deficit to dialogue has not been as complete as the narrative suggests [ 37 ]. More than ten years of research evidence has questioned the scale of this supposed shift and the extent to which the dialogue model goals and principles have been adopted in actual science communication practices [ 46 , 50 – 54 ]. The apparent replacement of the deficit model, as Wynne [ 46 ] concludes, is more nominal than real. Thus, even though the science communication vocabulary has changed considerably over time, the underlying assumptions may still be inline with those that inform a deficit model perspective [ 37 ].

In recent years, the narrative shift has evolved even further to include public participation or public engagement. Public participation (or public engagement) models have emerged as a direct attempt to enhance social trust in science policy. These models focus on a series of activities—consensus conferences, citizen juries, deliberative technology assessments, science shops, deliberative polling, etc. [ 55 , 56 ]—driven by a commitment to ‘democratize science’ through some form of empowerment and political engagement of the public. Although rarely linked in science communication literature, the public participation model shares many similarities to more established techniques, such as public meetings and public hearings [ 57 , 58 ].

Participation models signal a more obvious shift in power than the dialogue model and emphasise the role of the public and other societal stakeholders in reflecting upon, sharing knowledge about, creating new knowledge, and making decisions about science that affects society [ 9 , 10 ]. Participation models seek to set the sciences in a wider social context, addressing societal concerns and priorities that involve multiple stakeholder perspectives [ 38 ]. It is argued that such approaches to science communication are driven by the intent of ‘democratizing’ science by giving the public more control over science through some form of empowerment and political engagement [ 59 ].

Scholars have posed a number of criticisms related to participation models due to their focus on the process of science over any substantive content, their limitations in terms of the numbers of people they serve, and for sometimes exhibiting an ‘anti-science’ bias given their focus on lay/local knowledge over scientific knowledge [ 41 ]. The strongest criticism, however, relates to the fact that such models are said to address politics more than the public understanding of science due to their commitment to a particular stance about political relations [ 60 , 61 ].

The three science communication models discussed above, driven by different assumptions, provide only schematic tools for theorising about science communication activities. While the deficit model centres around filling a knowledge vacuum and the dialogue model on an exchange of perspectives between the sciences and the public, the participatory model centres on the ‘democratisation of science’ through some form of empowerment and political engagement of the public. The emergence of these models (and many others) together with the strong rhetorical shift from deficit to dialogue and participation—both in scholarly and policy discourses—seem to indicate a unidirectional transition process. However, Bucchi and Trench [ 4 ] suggest that rather than looking at a ‘fixed triad of deficit, dialogue and participation’ (p.8), this range of models should be seen as a dynamic spectrum that is continually growing in both directions.

While the radical transition—from deficit to dialogue and participation—and the concurrent rhetorical shift have been deemed highly implausible over such a short period of time [ 37 , 46 ], very few studies have endeavoured to empirically assess the specific goals scientists are trying to accomplish through their science communication efforts [ 27 ] and how their goals align with those of the deficit, dialogue, and participation science communication models. Although research has shown that many scientists accept and support dialog and participation as some kind of ‘gold standards,’ or are at least are aware of the push toward public communication [ 62 ], not many studies focused on assessing how this translates into actual science communication practices. One of the few studies investigating such practices found that—due to the continuing adoption of a simplistic contrast structure that opposes science and the public as two self-contained, antagonistic social entities—the shift towards more democratic engagement of the public has not been as profound and complete as expected [ 63 ].

In this article, we aim to address this research gap by investigating the goals scientists focus on in their science communication practices and the alignment of these goals with the three models discussed thus far. To do so, we build on the work of Metcalfe [ 30 ] that lists the primary goals inherent to each of the three science communication models, based on a thorough literature review. We further discuss these goals and their importance to our work in our Data and Methods section.

When discussing contemporary science communication practices, one must not omit to highlight the radical and important changes brought about by the emergence of social media channels. On a daily basis, millions of people all over the world are constantly consuming and creating content through social media platforms. Considering the popularity of such platforms (e.g., 192 million daily active users on Twitter), it is easy to see that information disseminated through these channels can reach millions in a matter of minutes and, as Van Eperen and Marincola [ 64 ] acknowledge, that successful communication can only be achieved by using the channels in which the public is currently engaged. Thus, social media channels offer a powerful venue through which scientists can act as a public voice for science in a quick and efficient manner by using a medium in which the public is already engaged [ 65 ].

Beyond reaching engaged and diverse publics—researchers, the general public, government, and all other stakeholders—social media platforms have the potential to enable multi-vocal, multi-way communication [ 39 ]. Such platforms are said to “facilitate interactive information sharing” [ 66 ] and to increase the depth and reach of engagement among stakeholders [ 67 ]. The potential of social media platforms to involve a wide variety of stakeholders in an interactional manner have long been emphasised by proponents of the dialogue and participation science communication models [ 39 , 68 ]. However, in reality, many researchers are cautious in changing traditional scholarly communication patterns in response to social media [ 69 ] and the area of online science communication remains understudied [ 65 ].

When it comes to science communication, although social media seems to be the ideal environment for two-way and multi-vocal communication models, the few studies that have investigated online science communication have found that this is only one part of the story. science communication practitioners and science organisations use social media for one-way message dissemination more often than they truly engage with their publics [ 70 , 71 ], and they generally underutilise social media’s potential to create true dialogue with their audiences [ 72 ]. For instance, when conducting a review of social media activities of 11 US based science agencies, Lee and VanDyke [ 73 ] found that their outreach activities did not facilitate two-way interactions very well, suggesting an adherence to a deficit-model way of thinking for these agencies. Similarly, analysing Twitter utilisation patterns of NanoDays science festivals organised between 2012 to 2015, Su and colleagues [ 74 ] found that most tweets exemplified a one-way, information-sharing model of communication.

Just as in the case of the proposed radical transition of science communication—from deficit to dialogue and participation—the belief that social media affordances foster unprecedented opportunities for two-way and multi-vocal science communication is becoming more or less an accepted fact. But, as the few studies that have investigated social media use for science communication have shown [ 73 – 75 ], one-way, information-sharing practices aligned with the deficit model still dominate. So if social media offers such opportunities, how come scientists still engage in information transmission behaviours? Here, we argue that in order to answer this question, we need to first have a better understanding of the goals scientists are trying to achieve through their social media science communication practices. To bring a contribution to this under-researched area of science communication, we investigate not only the goals scientists consider as most important in the context of general science communication, but we also assess which goals they find important in the context of social media science communication. To do this, we used a modified version of Metcalfe’s [ 30 ] list, specifically reworded for social media. We will further discuss this list in our Materials and methods section.

Considering the promises of social media as a participatory environment where already-engaged audiences can easily be reached, we also aim to investigate whether those actively using social media for their science communication efforts may approach science communication from a more participatory oriented perspective. To do so, we aim to compare between social media users and non-users and their general science communication goals.

In sum, the aims of our work are four fold. First, we aim to contribute to a broader understanding and deeper insights into what science communication is, beyond conceptual definitions proposed by literature. Moving past the unnecessarily narrow and rigid science communication definitions proposed by various scholars, we aim to grasp the perspectives of some of the core actors in the field, scientists themselves. Second, we set to investigate whether the highly popularised—and theorised—rhetorical shift from deficit to participation is reflected in the goals scientists identify as important to their science communication efforts. In other words, we aim to understand the scientists’ goals and which of the models are at work in their science communication activities. Third, we aim to provide a stepping stone towards a better understanding of why a deficit-model way of thinking remains dominant in social media science communication efforts. Lastly, we aim to uncover whether those actively using social media for their science communication efforts may approach science communication from a more dialogue or participatory oriented perspective, rather than the deficit perspective shown by previous studies.

Materials and methods

Ethics statement.

The survey data for this study were collected in accordance to the guidelines of the Association of Universities The Netherlands [ 76 ] and ethical approval was not required. Under Dutch law, ethical approval is not required for conducting survey questionnaire research in the Netherlands when such surveys do not target children or other vulnerable groups and they do not address confidential or sensitive issues [ 77 ]. Furthermore, ethical approval for this study was deemed unnecessary by the check-list provided by the Social Sciences Ethics Committee of Wageningen University & Research.

Participants in our survey gave informed consent before their answers were recorded. The first page of our survey provided a full explanation of the scope and aims of our study and it informed respondents that participation is anonymous and voluntary. Furthermore, at the end of the survey, respondents were informed that by clicking the submit button they consent for their answers to be included in our study and that they could withdraw from the study at any time. To ensure the anonymity of the respondents, personal identifiers such as name, e-mail address, physical address, and organization name were not collected. Our respondents had the opportunity to provide us with their social media information (e.g., Twitter handle) for a later stage of this project and to leave an email address for a chance at winning a gift card. Their social media information and email addresses are stored separately from their survey responses and will not be included in any reports using these survey data.

Survey data

The data included in this study comes from a large scale survey we conducted in the Netherlands between April 1st and May 31st 2021. The survey was specifically addressed to researchers, at any career level, working in any public research or technical university or research institute in the Netherlands.

Our survey was disseminated via multiple channels in an attempt to collect a representative sample for researchers in the Netherlands. Invitations to participate, which included an anonymous link or a QR-code, were placed in several university-based magazines (online and in print), newsletters, and intranet pages. We also posted similar invitations on Twitter, Facebook, and LinkedIn. Lastly, for every university in the Netherlands, the survey was disseminated via email, based on an email address list generated using the public online directories provided on each university’s websites, for each of their faculties. Alongside the universities, five publicly funded research institutes were also targeted.

While our email address list included over 20,000 emails, it did not capture those scientists whose contact information may not have been provided online or was not updated in the university contact directories. In order to limit inconveniencing our potential respondents, no reminder emails were sent after the first invitation email. Of the total of 584 respondents who completed our survey, 2 of our respondents accessed the survey via the QR-code, 10 via social media links, and 572 via email invitation links. While a precise response rate cannot be calculated, the completion rate for those who started the survey was 59.65%.

The full survey—comprised of 32 questions delivered with a mixed-method approach (i.e. open-ended and closed)—asked participants a range of questions related to their understanding and involvement in science communication activities. The first 17 questions in the survey, addressing general science communication issues, were answered by all our participants ( N = 584), while the remaining 15 focused on science communication in the context of social media. These 15 questions were answered only by those participants who indicated that they use social media for science communication purposes ( N = 314).

For the study presented in this article, we focus on a subset of questions from the survey discussed above. The subset of questions align with the aims of this article to investigate personal science communication definitions, to assess the extent to which the proposed shift from deficit to participation is reflected in the goals scientists identify as important to their general science communication efforts, and to assess the goals scientists identify as important to their social media science communication efforts. In the following paragraphs, we discuss the questions selected and the methods employed to analyse them.

Personal definitions of science communication

An open-ended question asking participants to define science communication in their own words was used to gain a broader understanding of our participants’ individual viewpoints (or frames of reference) on what science communication is. In answering this question, participants were instructed to use either sentences or keywords to tell us what their personal definition of science communication is.

To analyze the ways in which our respondents define science communication, we adopted semi-inductive, qualitative thematic analysis. A widely used method in qualitative research, thematic analysis is a method to identify, analyze, and report patterns found in data [ 78 ]. While thematic analysis can be conducted in both an inductive and a deductive manner, here, we refer to a semi-inductive approach because although the coding emerged from the responses given by our participants, the coding topics were partially informed by the coders’ theoretical and conceptual knowledge in the field of science communication. For instance, definitions highlighting the “communication of scientific facts to the non-scientific communities” were given the label ‘Transmission’, which aligns with the one-way, top-down communication process prescribed by the deficit model.

Following the thematic analysis phases described by Braun and Clarke [ 78 ], the first author (1) familiarized themselves with the data by reading and re-reading the 584 definitions provided by our respondents. Next, the first author (2) inductively generated initial codes across the entire data set. This second step in the analysis was conducted until no further codes were found to be emerging from the data. During this iterative stage, codes were developed and merged, and clear definitions were created for each of the codes. Each of the 584 science communication definitions was assigned one or more of these codes, based on the types of communication the definition describes. A single code was not applied multiple times to the same definition. Once no new codes were found to emerge from the data, the first author proceeded to (3) collate the 30 codes that emerged from the data into six potential themes, (4) review the themes and their relation to the codes, and (5) name and refine the specifics of each theme. All the themes and codes, together with their definitions and in vivo examples, were collected into a codebook. Using this codebook, the second author also coded 100% of the data. Because the two authors coded the entire data set and because predefined quotations were not used for coding, we report Holsti’s index [ 79 ]. The inter-coder agreement between the two coders was 84.9%. 5.9% (35) of the definitions provided by our respondents were either too short or ambiguously formulated so that they could not be coded.

Lastly, the two authors discussed and reviewed the initial themes to insure clear and identifiable distinctions between them as well as meaningful coherence for each theme. To further confirm the reliability of the themes, Holsti’s index was calculated for each of them: Type of communication (87.7%), Audience (93.2%), Content (87.0%), Media (96.4%), Goals (77.2%), and Impact (79.2%). After this review, all the six themes initially identified were kept and included in our analysis.

General science communication goals and models

To assess the goals respondents find most important to their science communication efforts and how these goals align with the conceptualisations proposed by the deficit, dialogue and participation models, we used a 28 item scale. Participants were asked to rate the importance of the 28 items to their science communication goals on a 5-point Likert-type scale, ranging from 1 = ‘Not important at all.” to 5 = “Extremely important.” This question appeared in the survey after respondents were asked to defined science communication in their own words.

The 28 items in our scale are based on the extensive literature review published by Metcalfe [ 30 ]. In her work, Metcalfe [ 30 ] used this 28 item scale with similar aims to ours: to assess theorised models against real-life science communication practice. However, her work, centred on the specific context of a 2012 Australian audit and 515 science engagement activities, found that most engagement activities had objectives that reflected a mix of deficit and dialogue activities and a lack of participatory activities in Australia [ 30 ]. Rather than focusing on activities, in our work, we will employ the 28-item scale to assess the goals our respondents find most important to their science communication efforts. Based on the literature review conducted by Metcalfe [ 30 ], each of the 28 items in the scale (listed in Table 1 ) align to one of the three science communication models as follows: goals 1 to 14 align with the deficit model, goals 15 to 20 align with the dialogue model (15-20), and goals 21 to 28 align with the the participation model. To ensure that the 28 items form a reliable scale, both as a whole and as three separate scales aligned with each of the three science communication models, Cronbach’s alphas were calculated for each. We find that the 28 items list as a whole (Cronbach’s α = .925) and also divided according to the three models form reliable scales (see Table 1 ).

To verify if the 28-items in our scale align to the three science communication models, as proposed by the literature review conducted by Metcalfe [ 30 ], we used factor analysis. More specifically, we employed confirmatory factor analysis (CFA), a statistical technique used to verify that the theory-postulated relationships between observed variables and their underlying latent constructs exist [ 80 ]. The CFA was conducted using AMOS (version 26.0.0). First, we confirmed the 28 items in our scale are suitable for factor analysis through a Bartlett test ( χ 2 (378) = 7762.743, p = < .000) and the Kaiser–Meyer–Olkin measure of sampling adequacy (KMO = .913). Next, factor loadings were assessed for each of the 28 items and three items with factor loadings less than the acceptable threshold of 0.30 [ 81 ] were removed from the analysis. Thus, from the initial 28 items presented in Table 1 , items 6, 11 and 21 were removed from further analysis, which resulted in a final scale of 25 items. Strong factor loadings were found across the remaining 25 items, ranging from.42 to.71. Next, we evaluated the three-factor model fit using the relative Chi-square ( χ 2 / df ) [ 82 ], the comparative fit index (CFI) [ 83 ], the goodness of fit index (GFI) [ 84 ], the standardized root mean square residual (SRMR) [ 85 ], and the root mean square error of approximation index (RMSEA) [ 86 ].

Our results confirm the unidimensionality of each construct in our model and indicate that the measurement structure of three factors (Deficit, Dialogue, and Participation) and 25 items produced good fit statistics (see Table 2 ). As the sample size of our study is relatively large ( N = 584), it is within expectation that the model did not pass the χ 2 test ( p < .05) [ 87 – 89 ]. Lastly, we found that even after the removal of the 3 items, the remaining 25 items form reliable scales, both as a whole (Cronbach’s α = .918) and as three separate scales aligned with each of the three science communication models: Deficit model Cronbach’s α = .830, Dialogue model Cronbach’s α = .817, and Participation model Cronbach’s α = .855.

In our Results section, we explore these 25 general science communication goals individually (i.e., based on their item means across all respondents) as well as partitioned according to the three science communication models they represent, as confirmed by the factor analysis reported above. For the latter, we computed two aggregate variables for each of the three latent variables/models (Deficit, Dialogue, and Participation): 1) the unit weighted scale means and 2) factor scores using factor score weights produced by the CFA, normalized to allow for comparison across models [ 90 – 92 ]. While the bulk of the statistical analysis in this paper will focus on factor scores, the results from the scale means are also reported; these have been argued to hold complementary utility that is less dependent on sample characteristics (e.g., replicability of findings across studies [ 93 ]). After we computed the pairs of variables for each respondent and communication model, Shapiro-Wilk tests [ 94 , 95 ] showed a significant departure from normality for all of our variables (see Table 3 ). Due to the variables’ non-normal distribution, and because the variables from each model are based on related samples, we used Wilcoxon signed-rank tests to determine whether the differences in how our respondents rate goals of the three science communication models are statistically significant [ 96 , 97 ]. As a non-parametric statistical test that compares two paired groups, the Wilcoxon signed-rank can be used as an alternative to the t-test when the population data does not follow a normal distribution [ 97 ].

Social media science communication goals and models

Additionally, using Metcalfe’s [ 30 ] scale, we selected nine of the 28 general science communication goals that were most suited to the particulars of social media science communication—three goals for each of the three models discussed—to create a scale for social media science communication goals (Cronbach’s α = .846). Although the items in this scale were specifically worded for the social media context, the question asked respondents to rate the importance of these items for their social media science communication activities, just as in the case of their general science communication goals. The nine items aligned to the deficit model (1-3), the dialogue model (4-6), and the participation model (7-9) and they form reliable scales (see Table 4 ).

Just as in the case of the general science communication goals, we ran confirmatory factor analysis to assess whether the nine items in our scale align with the three science communication models, as proposed by Metcalfe’s literature review [ 30 ]. We first confirmed that the 9 items in our scale are suitable for factor analysis through a Bartlett test ( χ 2 (36) = 963.682, p = < .000) and the Kaiser–Meyer–Olkin measure of sampling adequacy (KMO = .872). Next, and unlike in the case of the general science communication goals, no items were removed from the three-factor model due to strong factor loadings, ranging from.60 to.76. Using the same model-fit evaluation measurements ( χ 2 / df , CFI, GFI, SRMR, and RMSEA), we confirmed the unidimensionality of each construct in our model. Hence, a three factor model (SM Deficit, SM Dialogue, and SM Participation) with 9 items produced good fit statistics (see Table 5 ). Because our sample size is relatively large, the model did not pass the χ 2 test, as models based on large sample sizes have a higher tendency to be rejected [ 87 – 89 ].

Just as we did for the general science communication models, in our results section, we explored the nine social media goals individually, but we also computed latent variables for each model, using the factor score weights as well as the unit weighted means of each scale. An exploration of these three new pairs of variables (for each SM Deficit, SM Dialogue and SM Participation) also shows a statistically significant departure from normality, as shown by the Shapiro-Wilk tests in Table 6 .

Before describing our findings, let us first describe the demographics and characteristics of our survey respondents. 54.6% of our respondents identified as female, 43.2% as male, and 0.9% as non-binary/third gender, while 1.4% preferred not to say. Most of the respondents in our data set held a PhD (58.6%) or a Masters’ (38.9%) degree, with 77.1% being employed under a full time contract. Interestingly, as per Table 7 , the majority of respondents in our sample were early-career researchers (including 37.8% Ph.D. candidates and 11.8% Postdoctoral researchers). We further elaborate on the implications of this finding in our discussion.

In the following subsections, we discuss our findings regarding our respondents’ personal science communication definitions, their general science communication goals, their social media science communication goals, and how these goals align with the three science communication models discussed in the introduction of this paper, namely the deficit, dialogue, and participation model.

The thematic content analysis conducted on the 584 definitions provided by our survey respondents resulted in 30 codes and 6 themes. These codes and themes highlight the various science communication aspects, dimensions, and perspectives our respondents highlighted in their definitions. In Table 8 , we show our codes, their occurrence frequency, and an example for each code as well as the themes emerging from collating our codes. Here, we should note that a code was applied to each definition provided by our respondents only once. In Table 8 , the codes are organized by theme and ordered by frequency.

Overall, our respondents highlighted six main dimensions of science communication, as shown by the themes that emerged from their definitions. These themes center on the type of communication ( N = 620), the audience addressed ( N = 511), the content of the communication ( N = 389), the goals to be achieved ( N = 148), the medium through which the communication takes place ( N = 57), and the impact of science communication ( N = 30). We will discuss each of these themes and their most prominent codes individually.

Type of communication

Within the type of communication theme, the most prominent dimension we uncovered during our coding was that of information transmission/transfer. As shown in Table 8 , most definitions (63.7%) made mention of science communication as an information transmission process, using terminology such as disseminating, transmitting, or transferring scientific knowledge or information to an audience. As we will discuss later, this process of transmission was highlighted regardless of the type of audience addressed. The second most prominent emerging dimension was that of translating scientific information (27.57%), while dimensions related to more dialogic, engagement, or participatory dimensions of science communication emerged in a very limited number of definitions. Thus, the focus on information transmission and translation seems to suggest that most of our respondents consider science communication to be a one-way process of information diffusion, from the knowledgeable scientist to the uninformed audience.

Our qualitative analysis showed that the next most prominent theme our respondents focused on when defining science communication is the audience to be addressed. While most respondents mentioned a lay or non-academic audience (62.16%), a good proportion also included academic audiences in their definitions (23.29%), while very few made reference to interested (1.20%) or uninformed audiences (0.86%). In sum, when defining science communication, our respondents identify academic as well as non-academic stakeholders as main audiences for their science communication efforts. 111 (19.01%) definitions included both, academic and non-academic audiences. As we will discuss later, these definitions may be indicative of a dichotomy, a separation between scientists and other members of the public, which has further implications.

The third most common theme emerging from these personal definitions of science communication was the content of their communication efforts. Here, most respondents emphasized research findings or results as the most common dimension of their definition (42.81%). Fewer respondents made mention of involving their audience in the process of science (11.30%) or of communicating the relevance (7.02%) and methods (5.48%) of scientific research. Considering our earlier finding—that most personal definitions we investigated focused on a one-way, information transmission model of science communication—it then come as no surprise that the most predominant content mentioned was research results. This would suggest that our respondents look at science communication as a process of transferring a ready-made product of science (results) to their audiences, whether academic or not.

Our analysis also uncovered a number of goals highlighted in the science communication definitions provided by our respondents. The most prominent of these goals was that of making science more accessible (5.48%), educating their audience (4.11%), raising awareness about science and scientific topics (3.94%), and enhancing interest in science (3.60%). The other six goals emerging in our analysis (see Table 8 ) were far less frequently mentioned by our respondents. As we will discuss later in this paper, most of these goals are very much in line with the deficit model of science communication, with the exception of the goal of making science more accessible, an inherently dialogue-oriented goal [ 30 ].

The fifth theme uncovered by our analysis was that of the medium through which science communication takes place from the perspective of our respondents. Here, 5.82% definitions mentioned traditional media (e.g., newspapers, radio, TV etc.) as the primary channel for science communication, while 3.94% made mention of digital and social media (e.g., blogs, Twitter, podcasts) as science communication platforms. This suggests that among those who include a means or channel of communication in their definitions, the majority made reference to more traditional forms of media, and fewer see digital and social media as a science communication venue.

The last and least prominent theme that emerged from our analysis was that of impact. The definitions formulated by our respondents included communicating about the general (2.57%), societal (1.88%) and practical (0.68%) impact of their research.

To summarize, the qualitative thematic analysis of our respondents’ personal science communication definitions aimed at creating a broader understanding of what science communication is, from the perspective of those who are closest to the scene. While not trying to discount the very few personal definitions that have included aspects of dialogue-based and participatory science communication, our results suggest that the majority of these definitions are centered around a deficit way of thinking by emphasizing a one-way, transmission model of communication that aims to transfer and translate research outcomes and its impacts to a non-academic audience, with the purpose of making science more accessible, of educating audiences, and/or of raising awareness about science.

Having investigated how our participants define science communication, in the next sections of our paper we quantitatively analyze our respondent’s general science communication goals, as well as the goals they find most important when specifically asked about social media science communication. Furthermore, we discuss how these goals aligned with the deficit, dialogue, and participation models of science communication.

Science communication models

We start the description of our quantitative analysis results with the ways in which our respondents rated the importance of the 25 general science communication goals and the science communication models they represent. Our results indicate that, on average, our respondents rated deficit model goals as more important than dialogue model goals and participatory model goals. In Fig 1 , we show the means of each of the 25 goals, in descending order and colored by the communication model they align with. The highest rated goals in our scale were deficit model goals. Based on the rating of our respondents, increasing understanding of science, educating, transferring information, raising awareness, and correcting misunderstandings or misconceptions were the most important goals to their science communication efforts. This particular finding resonates with our qualitative analysis findings, in that it seems to indicate that a deficit way of thinking was the predominant approach to science communication among our respondents.

The means of each model—as already hinted at by our qualitative results and by looking at the highest rated goals—indicate that our respondents did indeed rate deficit model ( M = 3.81) and dialogue model goals ( M = 3.66) as more important than participation model goals ( M = 3.38). A Wilcoxon signed-rank test of the factor scores further confirm that our participants rated Deficit model goals higher than Dialogue model goals and that this difference is statistically significant: T = 133157; z = −12.530; p < .001. We also find that on average, our participants rated Deficit model goals ( T = 138057; z = −13.757; p < .001) and Dialogue model goals ( T = 139912.50; z = −14.221; p < 001) significantly higher than Participation model goals. The same significance levels are also found between pairs of models when testing the scale means rather than the factor scores ( p < .001 for each pair). Thus, we can infer that in terms of science communication goals, our respondents focused more on a Deficit and Dialogue perspective, rather than a participatory approach.

Analyzing the personal definitions and the goals our respondents find most important to their science communication activities and efforts has thus far provided a relatively straightforward portrayal of mostly deficit or dialogue oriented perspectives, with participatory approaches underrepresented or entirely lacking. However, considering the emergence of social media and the promises of such digital channels to be the ideal environment for two-way and multi-vocal science conversations, we move on to investigate whether those actively using social media for their science communication efforts may approach science communication from a more participatory oriented perspective.

First, we note that from our 584 our respondents, only 314 (53.77%) reported that they use social media for science communication. Our 314 participants who said they use social media for science communication reported that they used LinkedIn (38.9%), Twitter (33.4%), Facebook (15.8%), and YouTube (0.9%) for science communication in the past 12 months, and also other less frequently used platforms (e.g., Instagram). All of these most commonly used platforms among our respondents offer affordances that can be used to enter a dialogue with or involve already-engaged audiences into participatory science communication activities. Thus, in the next paragraphs, we assess what type of goals those participants who use social media for science communication rated as most important.

When looking at how our respondents rated their agreement to the 9 statements related to social media science communication goals (see Fig 2 ), we found that the highest rated goal is a dialogue model one, namely the goal of making science/scientists more accessible. Unlike in the case of general science communication, where respondents rated a deficit model goal as the most important, we see that when specifically focusing on social media, participants rated a dialogue model goal as most important. This would seem to indicate that perhaps in the context of social media, respondents moved from the deficit view to a more dialogue oriented one. However, the next two highest rated goals are related to raising awareness and transferring science information, two inherently deficit model oriented goals. Furthermore, we note that overall the highest rated goals (based on their means) were those of the Deficit model ( M = 4.04) followed by Dialogue model ( M = 3.90), while Participation model goals were rated lowest ( M = 3.47). To further confirm the significance of this finding, we performed Wilcoxon signed-rank tests on the factor scores of our model variables. The results confirm that, on average, our participants rated Deficit model goals significantly higher than Dialogue model goals, T = 31957.00, z = −8.025, p < .001, and significantly higher than Participation model goals, T = 36779, z = −11.452, p < .001. The tests also show that, on average, Dialogue model goals were rated significantly higher than Participation model goals, T = 38018.50, z = −12.332, p < .001. Significance levels are found to be similar when using means instead of factor scores as well. Thus, when specifically asked about their science communication goals in the context of social media, and even though social media promises to be an ideal environment for dialogue and participation, our respondents’ focus remained mostly on the deficit-oriented perspective.

Additionally, our analysis found statistically significant differences in how respondents who reported using social media for science communication ( N = 314) rate their general science communication goals, when compared to those who reported not using social media for science communication ( N = 270). Overall, the factor score means listed in Table 9 indicate that social media users rated goals associated with all three models higher than non-users, and the Mann-Whitney U tests for each pair further confirm that these differences are statistically significant. Thus, in all cases, social media users rated goals significantly higher than non-users. When using scale means and Mann-Whitney U tests, we find similar significance, with the exception of the Participation model goals ( p = .124).

* p < .05

This particular part of our analysis showed that when comparing between social media users and non-users, we found important differences. Interestingly, social media users rated their general science communication goals higher than non-users, perhaps indicative of higher activity among social media users in communicating science.

Using a mixed-methods approach, this article set to uncover scientists’ personal science communication definitions; the goals they find important to their science communication efforts; the alignment of these goals to the deficit, dialogue, and participation models of science communication; and whether a deficit-model way of thinking remains dominant in social media science communication efforts. In the following paragraphs, we discuss our findings, starting with the qualitative thematic analysis results, followed by our quantitative findings and in the context of previous research. We discuss whether the highly popularised—and theorised—rhetorical shift from deficit to participation is reflected by our results, and we argue that we should be cautious of recent claims that we live in a new era of dialogue, transparency, and participation in the realm of science communication.

Personal definitions

The field of science communication has grown considerably over the past decade, and so have the number scientific writings on what science communication is. As an inherently multidisciplinary field—concerned with answering questions related to science and society, science in the media, and the role of science journalists—science communication research has produced many different theoretisations, models, and definitions of what science communication is and how it should be practiced. The lack of clarity in defining science communication, and the absence of extensive research into the perspectives of those at the centre of science communication, have prompted us to investigate scientists’ personal definitions in order to better understand how science communicators themselves define it. Our analysis of the 584 definitions provided by our survey respondents showed that, for scientists, there were six dimensions of science communication: the type of communication employed, the audience addressed, the content of the communication, the goals to be achieved, the medium through which the communication takes place, and the impact of science communication. Based on these six dimensions, most of our respondents define science communication as:

A one-way process of transmission and translation of scientific results and their impacts towards a lay audience, via mostly traditional media channels, with the goals of making science more accessible, of educating audiences, and of raising awareness about science.

This definition suggests an alignment between our results and those of previous studies that found scientists tend to favour one-way communication with the public via the media, viewing engagement as chiefly about dissemination of scientific knowledge [ 23 ]. This approach to science communication assumes that the scientific information transmitted will be interpreted in similar ways by all the members of the audience and that the transmission of scientific knowledge will generate understanding and support of science [ 36 ]. Also, this one-sided, informative, sender-receiver communication style draws an intellectual boundary between scientists and the public, portraying the public as a passive recipient of information [ 63 ]. Furthermore, the focus on translation of scientific information suggests the scientist needs to simplify complex information so that it can become accessible to the scientifically uneducated. As previous research has shown, scientists are often aware of the need to present material differently between different discourses, audiences, and situations, yet when specifically moving from an academic to a non-academic audience, “their conception of the changes entailed […] were conceived only as ones of simplification.” [ 98 , p. 444]. While the process of engaging with non-academic audiences does often entail the need for translation or simplification, what is ignored when focusing only on simplification is the need to include political and ethical implications, aspects which are often excluded form scientific analysis but highly relevant and legitimate in non-academic contexts.

The focus of this definition on mostly non-academic audiences, as well as the distinction some respondents made between academic and non-academic audiences, suggests a dichotomy of “us-them.” As previous research has already suggested, this separation between scientists and other members of the public may indicate that scientists do not see non-academics as part of the scientific dialogue or debate [ 99 ] and that they see the public as homogeneous [ 23 ]. Viewing their audiences as a uniform group of non-experts leaves little room for “any relevant expertise outside of the scientific community, or for any intermediate degrees of scientific knowledge or understanding.” [ 98 , p. 437] However, in today’s knowledge societies, the boundary between experts and non-experts is more fluid than ever [ 100 ], with publics asking to participate in the processes of knowledge production more and more and asking for that process to be made more transparent and available for public inspection [ 101 ]. The continued adoption of a simplistic contrast between science and the public as two completely separate and ‘antagonistic’ entities hinders a true shift towards more democratic public engagement [ 63 ].

Thus far, the emphasis on transmission and translation and the clear distinction between academic and non-academic audiences, together with the goals to educate and raise awareness, seem to indicate a mostly deficit driven approach to science communication among our respondents. The deficit way of thinking, or the deficit model of science communication, is guided by the belief that the public has a deficit in scientific knowledge that the scientist needs to fill through dissemination or education [ 38 ]. However, the science communication definition formulated by our respondents also includes elements of a more dialogue oriented perspective, by formulating the goal of making science more accessible. The dialogue way of thinking, or the dialogue model, centres on science communication as a two-way flow of information from expert to layperson and vice versa. While some may see the goal of making science more accessible as an inherently deficit oriented goal, we argue that this goal is the first step towards a dialogue model perspective. Making science and scientists more accessible creates opportunities for dialogue between scientists and the public to discuss scientific issues and discover public opinion [ 30 , 102 ]. We do note that other elements of the dialogue model were not at all prominent in the science communication definitions we analysed.

Science communication goals

The next aim of our investigation was to understand the goals scientists rate as most important, both in a general science communication context as well as a social media context. Scientists’ specific goals are crucial in understanding not only what scholars want to get out of the time and resources they invest into communication, but also in understanding their approach to science communication. When assessing the goals our respondents found to be most important for their general science communication efforts—and how these goals reflect the theorised science communication models—we found similar results as with their science communication definitions. Our respondents rated goals aligned with the deficit and dialogue models of science communication to be the most important.

In the context of social media, we found that our respondents’ focus remains mostly on the deficit-oriented perspective, which seems to be in direct contrast with the promises of social media as interactional spaces, ideal for two-way and multi-vocal science communication activities [ 39 ]. Perhaps, as Weller [ 69 ] noted, this is a result of the reluctance scholars have shown in changing established science communication patterns in response to social media. However, as other studies have also found, focusing on achieving mostly deficit oriented goals in the context of social media leads to an underutilization of social media’s potential to create true dialogues and engagement with their audiences [ 70 – 72 ].

Over the last two decades, scientific as well as funding and policy initiatives, in many countries, have shifted their rhetoric to move away from deficit ‘public awareness of science,’ and ‘science and society’ towards ‘citizen engagement,’ ‘dialogue,’ and ‘science in society.’ This change in terminology was suggested to be indicative of a transition from a deficit perspective on science communication, towards dialogue and participation. However, many have deemed this shift to be highly implausible over such a short period of time [ 37 , 46 ] and that most science communication initiatives are still guided by the misconception that deficits in public knowledge are the driving force behind conflict over science [ 36 ]. Even those outreach projects and activities that claimed to follow other theoretical approaches have been shown to use the deficit model approach as a backbone [ 29 ].

With this study we add to research evidence that has questioned the degree to which science communicators have truly adopted the goals of this proposed shift towards dialogue and participatory science communication [ 50 , 51 , 103 , 104 ]. Our results, though limited to scientists in the Netherlands, showed a predominant focus on the deficit model of science communication, with limited elements of a more dialogue centered approach and almost no emphasis on participatory approaches.

This narrow emphasis of the deficit approach does not allow for the recognition that scientific knowledge is not the only factor influencing how individuals reach judgments. Decades of research have shown not only that science literacy plays a very limited role in shaping public perceptions and decisions, but also that people with limited education can quickly come to understand highly complex technical information [ 42 , 105 ]. Furthermore, simply sharing science information in a unidirectional fashion is not an adequate way of increasing science knowledge or changing attitudes about science [ 106 , 107 ]. Taking a deficit approach to science communication presents a major obstacle for reflective exchange and mutual learning, and ultimately for the transition towards more democratic forms of public involvement [ 63 ].

As noted in our results and conclusion, our data also showed evidence of more dialogue-driven approaches among our respondents. This dialogue model perspective was evident in both the ways in which respondents defined science communication and in the general science communication goals they rated as most important. However, while the dialogue perspective is driven by aims of engaging in two-way conversations, this dimension was not prominent in our data. Rather, our participants highlighted the importance of making science and scientists more accessible. Because our respondents consider being and making science more accessible as important goals, perhaps a transition away from dissemination and towards an exchange of perspectives between scientists and the public is not out of the question. As Metcalfe [ 30 ] also argues, making science and scientists more accessible may make it easier for the public to engage with science, and it could be considered a perquisite for more sophisticated dialogue activities that have the potential to open up productive and surprising discussions [ 108 ].

Arguably, thus far, our discussion may read as yet another pejorative scholarly discussion about the deficit way of thinking that argues for a clear separation between deficit, dialogue, and participatory science communication approaches. This is not our intention and such a discussion would be rather unhelpful for the development of science communication. Rather, the few elements of dialogue-driven science communication approaches, together with the prominent deficit elements uncovered by our study, lead us to argue that scientists do not adhere to a single model of science communication and that a clear separation between these models is not realistic. Instead, as other studies have also found, a combination of goals and practices—inherent to different models—govern science communication efforts and perspectives [ 30 , 52 ].

What is of great concern, however, is that participatory aspects of science communication were nonexistent in most of the definitions formulated by our participants and that participatory goals were invariably rated the lowest, whether in a general or a social media context. Here, we are not trying to argue that deficit or dialogue model based activities are inherently inferior, nor do we want to suggest that such activities should be entirely abandoned. Rather, we would like to emphasise the importance of true engagement with various publics, especially when dealing with the complexities of current contemporary issues. Even when deficit model approaches are at the backbone of such science communication activities, participatory elements can be invaluable tools for reflecting upon science and knowledge, creating new knowledge, and most importantly, placing science in a wider social context [ 10 , 38 ].

This lack of focus on participatory goals among our respondents is especially problematic considering the demands of contemporary knowledge societies and in the face of fast-paced technological advancements. The rise of digital platforms and social media have created an entirely new ‘ecology’ of communication between science and the public [ 109 ]. In the past scientists and science organizations have relied on science journalists or professional communicators to interpret scientific information for the general public. While to a certain extent this still holds true today, digital media platforms have made it possible for scientists to communicate directly with publics [ 110 ]. This digital transformation of science communication comes with both positive and negative consequences. On the positive side, digital platforms can be used to facilitate broader involvement of citizens in science discussions and they can allow individuals to learn about science and to become involved in collective decision-making [ 111 ]. On the negative side, the abundance of information sources available to individuals and the prevalence of misinformation in digital media create an urgent need for science-society relationship and trust building [ 112 ].

While it may still be possible to communicate scientific findings in accordance with the traditional deficit model, it has become increasingly difficult to generate public trust in science on the basis of pure scientific testimony alone [ 100 ]. As Elam [ 100 ] states, in contemporary knowledge societies the challenges facing science communicators are no longer those of combating public ignorance, but rather those of creating “new forms of community around radical programmes of research still in the making.” (p. 251) Scientists need to re-frame their view of technology and digital platforms and consider their uses in more robust, relationship building activities [ 110 ].

The findings presented in this study are based on the results of a survey administered among scientists in the Netherlands. As it is with most surveys, and even though we made great efforts in disseminating and publicizing our study, only 584 complete responses were collected. Thus, our results and the subsequent discussion should be viewed in the context of a limited number of responses provided by scientists in the Netherlands.

Additionally, we saw that a large number of our respondents were early-career scientists (Ph.D. candidates and postdoctoral researchers). Previous studies discuss a shift in the culture surrounding more participatory approaches to science communication to be driven by early-career scientists [ 113 ]. However, our findings—although based on a sample in which early career scientists are over-represented—suggest an overall adherence to deficit model approaches, with some elements of dialogue. For a discussion on some of the factors (institutional and personal) that may impact the lack of more participatory oriented approaches to science communication, in the context of the United States, see [ 113 , 114 ]. Moreover, while Howell and colleagues [ 115 ] found that both late and early career scientists held positive views on the role of social media in providing opportunities to engage with the public, our respondents’ focus nevertheless remained on deficit-oriented goals when asked about their social media science communication goals. While we can only speculate that perhaps the differences between the academic contexts of the Netherlands and of the United States might be the underlying cause of these differences, further research comparing academic contexts and science communication approaches among scientists (much like the one conducted by Kessler and colleagues [ 21 ]) could shed more light on the issue.

The demographics collected in our survey did not include information about the affiliations of our respondents. Although this particular choice in the survey design offered respondents more anonymity, our results cannot speak to differences between scientists affiliated with different universities in the Netherlands, nor can they make distinctions between scientists from different disciplines. Previous studies have found that scientists’ academic fields play a role in their approaches to science communication. For example, Burchell found that participatory approaches to science communication was considerably higher in fields like the arts, humanities, and social sciences compared to the STEM (Science, Technology, Engineering, and Mathematics) fields [ 116 ]. Similar results are also reported by Kessler and colleagues, with scientists from the humanities, social sciences, and life sciences being more likely to adhere to dialogue and participatory perspectives [ 21 ].

Our study builds further evidence that a deficit model approach to science communication is still very much present [ 73 , 74 , 99 , 117 ], even in the face of increasing calls towards science communication approaches that foster dialogue and public participation [ 36 ]. Simis and colleagues [ 99 ] discuss four lines of reasoning for why the deficit way of thinking remains very much present in science communication. Their essay highlights the roles of scientists’ training in processing information in a rational manner, the lack of formal public communication training, viewing the public as “others”, and the fact that the deficit model works well for policy design as some of the most prominent reasons for the persistence of the deficit model.

Understanding that scientists are focused mostly on dissemination and translation of research results, with a willingness to make themselves and science more accessible, has important consequences for scientific organizations, the shaping of their long-term science communication strategies, the development of training programs for established scientists, and the inclusion of science communication curricula in graduate education programs. As Madden, Cacciatore, and Yeo [ 118 ] observe, without continued training in positive communication methods, grounded in social science research, “it is not surprising that scientists would follow the admittedly intuitive deficit model.” (p. 403) The importance of continued training in positive communication methods as well as the importance of engaging community members around scientific issues are also emphasized by Simis and colleagues [ 99 ] as ways of moving beyond knowledge deficit approaches.

Lastly, the abundance of science communication models proposed by many scholars over the years could not all be addressed in our work. For example, one established model we did not address is the contextual model [ 25 ]. This model is generally tied to particular audiences, focuses on particular context-dependant situations (e.g., locations), and highlights the ability of audiences to quickly become knowledgeable about relevant topics [ 29 ]. However, while this model highlights the importance of contextual factors in how individuals receive and process information, it nonetheless conceptualizes the same focus on the responses of individuals to scientific information, just as the deficit model does [ 119 ]. Another model that was not included in our investigation is the one described first by Scheufele [ 120 ] and later by Akin and Scheufele [ 121 ], namely the communication in context model. Also known as the science communication as political communication model, this expanded model of science communication takes into account the larger political contexts of science communication, acknowledges that scientists are just some of the many voices involved in scientific debates, and highlights the many science-related streams of information the public is exposed to. As Scheufele [ 120 ] argues, answering questions about emerging technologies will require “democratic decision making that draws on moral values, that weighs complex political options, and that includes debates about the ethical and legal aspects” (p.13590). Thus, it is reasonable to argue that such models can help us move beyond deficit perspectives and that they better fit with the realities of our contemporary societies and the complexities of current scientific advances. We encourage future studies to include such models in their investigations.

To conclude, science communication and public engagement can no longer be focused on persuading the public on the ‘facts’ of science, as we live in an era where most science debates are no longer small, localized events, controlled by scientists or science communicators. As such, and as Nisbet and Scheufele [ 36 ] also proposed, contemporary science communication and public engagement activities should move away from a deficit, top-down perspective, towards promoting conversations that “recognize, respect, and incorporate differences in knowledge, values, perspectives, and goals.” (p. 1777)

Funding Statement

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

• Open access
• Published: 16 July 2024

Interprofessional education in cancer care – a scoping review

• Virpi Sulosaari   ORCID: orcid.org/0000-0002-0898-3297 1 ,
• Nikolina Dodlek   ORCID: orcid.org/0000-0002-0534-721X 2 ,
• Andreas Brandl   ORCID: orcid.org/0000-0003-1990-2584 3 ,
• Johan De Munter 4 ,
• Jesper Grau Eriksen   ORCID: orcid.org/0000-0002-1145-6033 5 ,
• Wendy McInally   ORCID: orcid.org/0000-0002-9900-4612 6 ,
• Niall O’Higgins 7 ,
• Kim Benstead   ORCID: orcid.org/0000-0002-5106-7438 8 &
• Celia Díez de los Ríos de la Serna   ORCID: orcid.org/0000-0003-2630-2106 9  

BMC Medical Education volume  24 , Article number:  767 ( 2024 ) Cite this article

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Comprehensive cancer care requires effective collaboration by interprofessional healthcare teams. The need to develop educational initiatives to improve interprofessional collaboration is increasingly recognised. However, there is no agreement regarding the interprofessional competencies required for effective cancer care leading to much variation on the focus of research, planning and managing change. A scoping review was conducted to identify the current status of IPE in cancer care and to summarise the results of previous research in order to guide the development of interprofessional education in cancer care.

The JBI Scoping Review guidelines were used to guide the process of the review. A search of the available literature was conducted in CINAHL, MEDLINE (Ovid), PubMed, PsycInfo, Scopus databases from January 2012 to March 2023 to investigate IPE for health professional clinicians working in cancer care.

Of the 825 initial references and 153 studies imported for screening, a total of 28 studies were included in the final review. From those studies, seven focused on the need for IPE and interprofessional competence for oncology healthcare professionals, four reviewed existing IPE programs and 17 described the development and evaluation of interprofessional education. Findings show variation and lack of concept definitions underpinning research in IPE in cancer care settings. Variation also exists in the range of research activities in IPE, most notably related to communication, teamwork and the development of interprofessional practice. The evaluation of impact of IPE is mainly focused on health care professionals’ self-evaluation and general feedback. Impact on patient care was only evaluated in one study.

Conclusions

Based on the results, interprofessional education research in the field of cancer care is limited in Europe. Thus, there is a significant increase in publications in the last five years. A more systematic focus on the theoretical framework and definition of concepts would be of value. Research and programme development should be based on a shared understanding on what constitutes the interprofessional competences and IPE. Programmes to develop interprofessional practice should be developed and implemented systematically with inclusion of validated assessment methods, and evaluated and improved regularly.

Peer Review reports

Over the last decade, there has been increased interest in developing educational initiatives to improve interprofessional collaboration and practice in the cancer care setting [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Non-communicable and life-style related diseases, including cancer, are among the biggest challenges facing EU health systems and countries, causing illness, premature death and associated social and economic costs. The number of cancer cases is expected to increase in Europe by 24% by the year 2035 [ 8 ]. As the demand in cancer care continues to increase, health systems require a workforce of educated oncology specialists and professions to provide continuity and sustainability of care. Current educational systems have yet to match all the requirements needed in cancer care [ 9 ]. Furthermore, quality cancer care requires effective collaboration by an interprofessional healthcare team [ 10 , 11 ]. People with cancer benefit when the health care professionals caring for them, not only collaborate, but strive to learn from each other [ 12 ].

Interprofessional collaboration can be defined as collaborative interaction among experts with different professional backgrounds involved in care of people with cancer and who share common goals [ 13 ]. Models vary across cancer units [ 14 ] and can involve professionals from different oncology specialties (radiation, medical and surgical oncologists) and disciplines (such as pathology), professionals from nursing and social affairs and allied health professions such as physiotherapists, psychologists, nutritionists and speech therapists [ 15 ]. Professionals from varying disciplines and professions have different knowledge bases, premises and competences for cancer care and interprofessional collaboration [ 10 , 11 ]. Interprofessional healthcare teams need to understand how to optimize the skills of their members, share case management and provide better health services to patients and the community. Such collaboration results in a strengthened health system and leads to improved health outcomes [ 12 ]. Furthermore, effective communication is important not only for patients but for the well-being of all healthcare professionals (HCPs) [ 16 ]. Thus, interprofessional practice requires effective leadership,administrative support [ 17 , 18 ] and continuous evaluation [ 18 ].

According to The Centre for the Advancement of Interprofessional Education (CAIPE) interprofessional education (IPE) concept can be defined as occasions when two or more professionals learn with, from and about each other to improve collaboration and the quality of care [ 19 ]. The primary goal of IPE is to improve patient care by better interprofessional collaboration [ 6 ]. This concept should be reflected in the training of cancer care workforce. In oncology, the concept of multidisciplinary care is an established part of the clinical practice [ 20 ]. Training of oncology specialists and professions needs to recognise the value of interprofessional care. Interprofessional collaboration has been seen as necessary for example in precision oncology [ 21 ] and radiation oncology [ 22 ], but IPE programmes vary substantially across countries [ 23 ]. In Europe, many professional societies provide opportunities for post graduate training for medical professions. The existing training curricula of ESTRO (European Society for Radiotherapy and Oncology), ESSO (European Society of Surgical Oncology and ESMO (European Society for Medical Oncology), all recognise the importance of interdisciplinary knowledge and understanding among specialists in radiation oncology, cancer surgery and medical oncology. Yet, gaps exist in mutual understanding among the three disciplines on interprofessional practice [ 24 ]. Very few training programmes in the European curricula for oncology specialists involve formal interdisciplinary attachments or integrated interprofessional approach on cancer care. These deficits limit both the scope and the value of interdisciplinary collaboration and the drive for better care. Interdisciplinary dialogue also drives standards and improves communication [ 24 ].

In 2010, the World Health Organization issued a report Framework for Action on Interprofessional Education and Collaborative Practice stipulating that teamwork is the first among interprofessional learning domains in a clinical setting, the others being roles and responsibilities, communication, learning and critical reflection, understanding of the needs of the patient, and ethical practice [ 12 ]. Interprofessional learning (IPL) has become a more prominent feature of health professional education at both pre-qualification and post-qualification levels. While the terms interprofessional learning (IPL) and interprofessional education (IPE) may relate to differing processes, with IPL focusing more on micro learning processes and IPE being more strongly reflective of an overarching educational framework, they tend to be used interchangeably in the existing literature [ 25 ].

Recent research relates to collaborative practice skills within clinical oncology [ 2 ] and radiation oncology [ 6 ] in determination of the impact and value of interprofessional learning [ 26 ] and interprofessional communication [ 27 ]. Among the barriers to successful implementation on interprofessional education, are the variations, both in definition and in concept, underlying research on IPE. Commonly used concepts are ‘interprofessional learning’ and ‘interprofessional education’. However, recently, the European Commission has launched the concept of ‘inter-specialty training’ to combine education and training of medical, nursing and allied health professionals in the cancer care setting [ 28 ]. The concept was defined later by McInally et al. (2023): “Inter-specialty training in oncology occurs when two or more specialties within professions collaborate by learning and interacting with each other during training in order to provide high quality cancer care” [ 29 ]. However, in order to understand the context of previous studies and to inform both training programmes and future research it was considered that a scoping review was required.

The objective of this scoping review was to describe the extent and type of evidence regarding interprofessional education (IPE) in oncology. The aim was to identify how IPE has been defined, how methodology underlying the research and implementation of the IPE has been utilised, describe the state of IPE in oncology. This review is part of a European collaborative project on inter-specialty training with the intention of providing useful training programmes across Europe and beyond (INTERACT-EUROPE).

Methodology

The current review followed the scoping review methodology. This type of evidence synthesis aims to systematically identify and map the breadth of evidence available on a particular topic, field, concept or issue. Scoping reviews can clarify key concept definitions in the literature and identify key characteristics or factors related to this concept [ 30 ]. The scoping review involved five stages: 1), Development of a scoping review protocol including research questions, and the purpose of the study; 2) Literature search on CINAHL, MEDLINE (Ovid), PubMed, PsycInfo, Scopus data bases [ 3 ], Selection of studies, 4) Data extraction, 5) qualitative analysis and presentation of results.

The questions guiding the scoping review were:

How has IPE been defined in previous research in cancer care?

What competences have been used to guide the curriculum development of IPE in cancer care?

What teaching, learning and assessment methods have been used in previous studies?

The PRISMA checklist extension for scoping reviews (PRISMA-ScR) was used to guide the process of the review. A detailed scoping review protocol was produced including databases and subject headings [ 31 ]. MESH terms, when applicable, were used to capture all the relevant literature. In consultation with a librarian, initial search titles and abstracts ( N  = 825) were reviewed by one researcher. All 153 items identified in this way were then downloaded into Covidence Systematic Review software (r) for further screening by three researchers. This process was followed by full text screening by the same researchers to identify articles that met the specified inclusion criteria.

Data retrieval protocol

Databases: CINAHL, MEDLINE (Ovid), PubMed, PsycInfo, Scopus

The PCC that featured the search: P(articipants) = oncology medical professionals (medical oncologist, radiation oncologist, oncology surgeon) and nurses, C(oncept) = inter-specialty or interprofessional education or interprofessional learning, C(ontext) = Cancer care setting

Inclusion: Quantitative, qualitative studies and systematic reviews; papers with focus on IPE/IPL, development of interprofessional collaboration and teamwork through IPE; teaching, learning and assessment methods of IPE in the context of oncology.

Exclusion: Editorials, discussion papers, focus on other healthcare professionals outside oncology setting, non-oncological professionals, only on pre-registration students, conference abstracts and proceedings.

Limits: English language, 2012–2022 original search, updated search – to April 2023.

The main search terms: clinical oncology [MESH], medical oncology [MESH], radiation oncology [MESH], surgical oncology [MESH], oncology nursing [MESH], interprofessional training, interprofessional education, interdisciplinary education, interprofessional learning, interspecialty training, inter-specialty training.

Any conflicting screening results were discussed and decisions were made collaboratively. Full-text articles were filtered and reviewed. A data extraction sheet was pre-planned to extract the key information of the studies and reviews including authors, year, country, purpose of the study or review, study or review type and method, concept used, definition of concepts, interprofessional learning or education focus and/or competences, programme characteristics, teaching, assessment, and evaluation methods used and the main results.The data were extracted by three researchers and analysed with narrative content analysis. The process, analysis and summary of results were further discussed with the full research group including all the authors.

Characteristics of the studies

A total of 28 studies were identified through database searching, of which one study was identified by reviewing reference lists (Fig.  1 ). There were fourteen quantitative studies, four reviews, six mixed methods studies and four qualitative studies (Table  1 ). The articles reported interprofessional educational programmes in United States ( n  = 15), Canada ( n  = 7), United Kingdom ( n  = 3), Denmark ( n  = 1), Germany ( n  = 1) and Switzerland ( n  = 1). Articles were published between 2012 and 2022. Nineteen of the articles were published in the last five years. From the included papers eight focused on describing the need and competences of oncology healthcare professionals [ 3 , 4 , 6 , 9 , 32 , 33 , 34 , 35 ], three reviewed existing IPE [ 1 , 36 , 37 ] or the development and evaluation ( n  = 17) of oncology interprofessional training [ 2 , 5 , 16 , 22 , 26 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. The target groups of the IPE included nurses, pharmacists, physicians (medical oncology, surgical oncology, radiation oncology and palliative care), radiographers, technicians and staff with healthcare backgrounds such as psychology, occupational therapy and other support workers (such as social workers, chaplaincy, or administration staff in contact with oncology patients).

Data retrieval

Collaborative care in cancer

In oncology, medical, nursing and allied health professionals provide complex care in an interprofessional context [ 33 , 37 , 41 , 45 ]. To provide the best treatment and care for people with cancer, healthcare professionals are required to collaborate [ 1 , 37 ]. According to Head et al. (2022) interprofessional collaborative practice is an essential component of quality healthcare in oncology [ 42 ]. Effective interprofessional care was seen as necessary to provide optimal care for patients [ 16 ], improve the safety of care delivery [ 38 ] and better outcomes of patient care [ 16 , 32 , 38 ].

Terminology

Eight of the papers [ 3 , 6 , 26 , 32 , 34 , 35 , 37 , 44 ] reported using the concept of IPE or IPL with reference to existing definition in literature. One study used the concept of “Interprofessional clinical training “ [ 38 ], one “Interdisciplinary education and training”, and one “Multidisciplinary education and training” [ 49 ], however no definition of these concepts were provided [ 36 ]. None used the concept of ‘inter-specialty or interspecialty education or training’. Nine of the included papers had some concepts described but did not include clear definitions and eleven had no mention or definition of IPE or IPL. (Appendix Table).

The concept of interprofessional education (IPE) can be seen as a means to improve health system function and delivery of care [ 34 ]. In order to achieve positive transformations in healthcare delivery, healthcare professionals (HCPs) must develop skills in interprofessional collaborative practice [ 42 ]. The principles of IPE should be embedded into every aspect of programs [ 36 ]. IPE would ideally result in greater understanding and improved communication between disciplines and professions [ 22 , 32 , 36 ], improved coordination [ 32 , 36 , 39 ], enhance team-based care management [ 32 ] and optimize more culturally affirming care [ 46 ]. Desired outcomes from IPE include also articulating one’s professional role as well as those of other professions, mutual respect, trust and willingness to collaborate [ 5 ].

Competency domains

In the field of oncology, increasing and building on a set of foundational knowledge, skills, and attitudes within physical, psychological, social/cultural, and spiritual domains, and collaborating with other HCPs, an early learner/novice practitioner will move towards an identity as an expert interprofessional practitioner. A competence framework on the shared set of competencies can bring professionals together, while recognizing the individuality of each profession as possessing distinct and complementary skills [ 9 ].

Of the papers describing competence framework development for interprofessional education, one focused on finding consensus on shared interprofessional competences in oncology [ 9 ], one on teamwork competences [ 32 ], one on integrative oncology [ 35 ], one on communication skills [ 36 ], one on cultural competence [ 46 ], two on paediatric oncology [ 1 , 37 ], one on palliative care in oncology [ 42 ], two on psychosocial training needs in oncology [ 4 , 33 ] and three papers described the specific needs of radiation oncology professionals [ 3 , 6 , 34 ]. Development of frameworks considered the challenges to effective coordination and the impact on patient and clinical outcomes as essential to optimal, high-quality care [ 32 ].

Four of the papers reported development competences for IPE. The development process was informed by guidance from an expert advisory panel with a Delphi study based on a literature review in two of the studies [ 9 , 35 ]. Both Esplen and colleagues [ 9 ] and Wells-Di Gregorio and colleagues [ 33 ] started from domains proposed by an expert subgroup, Esplen and colleagues [ 9 ] incorporating also focus group interviews. In the Warsi et al. (2022) study the focus group was used to determine intervention objectives [ 49 ]. The expert panels all involved oncology professionals, and one [ 35 ] included patient and public representatives. All included shared competences divided into the domains of knowledge, skills and attitudes.

Participants

Target groups included in six of ten studies multidisciplinary professionals working in general oncology [ 16 , 38 , 39 , 45 , 46 , 47 ], four in radiation oncology [ 2 , 22 , 26 , 44 ], one in gynaecology-oncology [ 43 ], one in paediatrics [ 48 ] and four in different departments within the hospital or in primary care [ 5 , 40 , 41 , 49 ]. Focus on the programmes varied. Thus, interprofessional collaboration and practice in general was included in the learning goals of the IPE in six papers [ 3 , 6 , 9 , 25 , 35 ], communication in five papers [ 9 , 16 , 26 , 35 , 39 ] and teamwork in [ 2 , 3 , 22 , 26 , 39 , 46 ] representing the main areas of interest of IPE in the cancer care setting.

Five studies described existing IPE education [ 6 , 36 , 37 , 42 , 46 ], while two focused on paediatric oncology [ 1 , 37 ] and one on interdisciplinary education [ 36 ]. Three of the studies used literature reviews to identify IPE [ 1 , 37 , 49 ] and one [ 36 ] got the information from a survey carried out by oncology physicians from different specialties.

Teaching methods

Teaching methods varied in methods and usefulness and included face-to-face and web-based didactic content such as lectures, workshops, educational sessions, role play and reflections. Three papers concluded that there is a lack of interdisciplinary education in oncology and also highlighted the value of IPE to professionals. (Table  2 ). Teaching varied in time from a one hour-long discussion group session accompanied by online modules [ 48 ] to a year-long course [ 44 ]. The mode of delivery also varied including simulated cases and scenarios ( n  = 5) [ 2 , 16 , 22 , 26 , 40 ] some specified having standardized patients [ 2 , 16 ] and others were cases discussed and developed in teams [ 22 , 26 , 40 ] and/or by use of self-reflection [ 16 ]. Five of the studies included e-learning modules [ 5 , 16 , 38 , 41 , 48 ] alone [ 38 , 41 ] or in combination with face-to-face training [ 5 , 16 , 48 ]; in the case of the other nine, all the training was face-to-face [ 2 , 22 , 26 , 38 , 40 , 43 , 44 , 45 , 47 ] All but one of the studies were focused on learners. One exception was based on a train-the-trainer model [ 39 ].

Of the 18 papers which described the evaluation of IPE programme (Table  3 ), 11 described also the development process [ 5 , 22 , 26 , 38 , 39 , 40 , 41 , 44 , 46 , 48 , 49 ].

In the evaluation of education programmes, ten used pre- and post-programme evaluation methods [ 5 , 16 , 22 , 26 , 38 , 40 , 41 , 45 , 47 , 48 ], two had mixed methods with observation and surveys [ 2 , 39 ], one used qualitative evaluation with semi-structured interviews [ 45 ] and one compared the professionals participating with participants from other education activities [ 44 ]. General feedback surveys with participant satisfaction were the most common programme evaluation surveys developed for the studies.

Studies included samples of between four [ 44 ] and 1,138 participants [ 41 ]. Three of the studies included three-month follow-ups [ 38 , 43 , 48 ] and three studies, six-month evaluation follow-ups [ 43 , 48 , 49 ] indicating that gained intervention outcomes were sustained in the long term.

The following instruments were used to evaluate the impact of the IPE: (i) Readiness for Interprofessional Learning Scale [ 3 , 22 , 26 ], (ii) UWE Entry Level Interprofessional Questionnaire [ 22 ], (iii) Trainee Test of Team Dynamics and Collaborative Behaviours Scale (CBS) [ 22 ], (iv) Assessment of Cultural Competence using the Intercultural Development Inventory [ 40 ], (v) Frommelt Attitudes Toward Caring of the Dying [ 40 ], (vi) Attitudes Toward Health-Care Teams Scale [ 3 ], (vii) Attitudes Toward Interdisciplinary Learning Scale [ 3 ], (viii) Self-Efficacy for Interprofessional Experiential Learning Scale and End-of-Life Professional Caregiver Survey [ 1 ], (ix) Cultural Competency Assessment (CCA), Lesbian, Gay, Bisexual, and Transgender Development of Clinical Skills Scale (LGBT-DOCSS), (x) Interprofessional Socialization and Valuing Scale (ISVS) [ 46 ].

Other studies included in the review describe Delphi methods [ 9 ] and focus group interviews [ 4 , 6 , 9 , 33 , 39 , 45 ] and instruments developed for the purpose of the study [ 5 , 38 , 39 , 43 ].

Participants had positive reactions to the programmes indicating them as a promising strategy in improving cancer care [ 38 ]. They reported high levels of satisfaction [ 26 ], including improved relations within the team [ 22 ], the acquisition of new skills [ 41 ] as well as cross-cultural competence [ 40 ]. Confidence among the participants also increased [ 41 ]. Participants reported that they would highly recommend these programmes to their colleagues [ 2 ].

Participants considered that these educational events were valuable. They helped in areas such as consolidating communication, improving dialogue [ 5 ], valuing leadership [ 42 , 44 ] and better understanding of spiritual needs [ 48 ]. These programmes also improved understanding of specific issues such as the effects of therapy on patients, the place of palliative care, management of pain and other symptoms and quality of life [ 47 ] and also a comprehension of the legal issues surrounding cancer [ 43 ].

There was s tatistically significant improvement in knowledge of teamwork principles [ 39 ] developing shared mental models, cross-monitoring situational awareness and effective conflict resolution, agreements about roles and responsibilities [ 22 ], and behaviours. Participants valued the opportunity to gain the perspective of other professions, connecting with colleagues from other disciplines practising crisis response in a simulated environment [ 2 ], and demonstrating lower levels of concern and anxiety when communicating with other professionals [ 44 ]. Some participants incorporated meditation into their daily routine by involving other family members and making it part of a “family routine” [ 45 ].

Significant improvement was also noted in increased comfort when discussing survivorship issues with patients. Significant increase in knowledge of survivorship care for five types of cancer, more confidence in ability to explain a Survivorship Care Plan (SCP), and increased comfort in discussing late effects of cancer treatment [ 47 ] were all reported. The main challenges were “breaking down the walls and being more comfortable with vulnerability” [ 45 ], and in being more open-minded after training [ 43 ]. Training increased IPE recognition of participants’ home institutions [ 42 ].

To the best of our knowledge, this work is the first scoping review summarising the existing research on interprofessional education and learning in the cancer care setting. We identified 28 articles published between January 2012 and March 2023 with a significant increase in publication in the last five years. The results indicate growing interest of interprofessional education in this setting. Interestingly, most of the studies and reviews identified were from US and Canada, showing the need for further research and collaboration in Europe.

This highlights and strengthens the need for collaborative initiatives and projects such as INTERACT-EUROPE, launched in 2022. The project is based on the EU Beating Cancer Plan 2021 in which the concept of “inter-specialty training” was launched to combine education and training of medical, nursing and allied health professionals in the cancer care setting [ 27 ]. This extends the use of specialty to also include different professionals, not only specialties among one profession [ 29 ]. The EU Beating Cancer Plan is a key strategy document for cancer care development across Europe, including the training of cancer care workforce. The concept will be used in the European training programmes. Therefore, it was important to understand its similarities and differences with most used concepts, especially, interprofessional education (IPE). One important aim of the review was to identify the concepts used in research and IPE development in the field. We found that interprofessional education [ 3 , 4 , 6 , 9 , 16 , 22 , 26 , 33 , 36 , 38 , 39 , 40 , 41 , 42 , 43 , 45 ] is the most common term used. However, it seems that, in the oncology setting, there is need for improving the theoretical underpinnings of education research on interprofessional education. Inter-specialty training and interprofessional education are very similar. Thus, inter-specialty as a concept could refer to education including only specialities of one profession. To enhance the interprofessional practice it is, however, important to be inclusive for all professionals of the multidisciplinary teams. From 11 studies and reviews using the IPE or IPL, only eight studies defined or described the concept. In the McInally et al., (2023) study, medical doctors and nurses from the oncology field, expressed lack of understanding of the inter-specialty training as a concept [ 29 ]. IPE may have been more familiar to participants; however, the results of the study and our review indicate that conceptual clarity is needed in future studies and development of inter-specialty training.

The illustrates the multidisciplinary context of cancer care settings and the need for professionals to work together for people with cancer to achieve optimal care outcomes [ 10 , 11 , 50 ]. Interprofessional education, bringing the professionals together, can improve interprofessional practice and collaboration. According to our results, participants evaluate IPE positively and improving the collaboration. To support the development of interprofessional practice in oncology, as described in the WHO Interprofessional Collaborative Practice framework, describing and defining the common competences for inter-specialty training programme is important [ 12 ].

The shared set of competences in oncology practice included interprofessional collaboration, recognition and understanding or needs and experiences of a person affected by cancer, person-centred care and service approach, communication skills, use of technology in care delivery and understanding of one’s own limits and ability for self-care. James et al., (2016) also highlighted the values and ethics, roles and responsibilities [ 2 ] and Koo et al., (2014) attitudes towards interprofessional collaboration, in addition to communication competences and teamwork capabilities [ 3 ]. The above-mentioned are also key elements of interprofessional education. The aim is not for learners to master all the disciplines and professional expertise, but more about understanding the expertise of different professions and what they need to know about the specialty or the profession to work efficiently together [ 2 ]. Thus, our review provides information also on the common foci of the programmes in addition to more general interprofessional practice and team work focus; care and symptom management [ 2 , 22 ], paediatric oncology (1, 36, 47 ), safety [ 38 , 39 ], psycho-social oncology and psycho-oncology [ 4 , 5 , 33 , 43 ], palliative and end-of-life care [ 1 , 40 , 43 ], spiritual care [ 48 ], integrative oncology [ 35 ], survivorship [ 41 , 47 ] and well-being at work of healthcare professionals [ 45 ]. Although, our review focused on oncology, findings on theoretical underpinnings of the studies and programmes, teaching methods and evaluation of IPE could be used on other specialties also.

A variety of methods was used on the IPE. The content and focus and learning goals of IPE, and what constitutes interprofessional education varied between the studies and reviews. This has been highlighted also as common to IPE by the Committee on Measuring the Impact of Interprofessional Education on Collaborative Practice and Patient Outcomes (2015) [ 51 ]. Programme evaluation was mostly limited on participant satisfaction and feedback on delivery of the education. The evaluation methods mostly used in evaluation of the impact of education interventions were knowledge tests, behaviour change, confidence, comfort, intention to change practice and self-assessment of preparedness, Previously validated instruments were used in five studies [ 3 , 22 , 26 , 40 , 46 ], but it was common to use survey instruments developed for the study. Impact on patient care was not often measured. In one study patient incidence reports were included [ 14 ]. This result demonstrates a need for more systematic use of previously developed evaluation and assessment methods when appropriate, but also extent the evaluation of impact on care.

Limitations

A limitation of this study is that only publications in English were included. Neglecting the potential data of studies from non-English speaking countries can have an impact on the results. Furthermore, we used broad subject headings with Boolean operators in search of literature, but it is possible that we did not find all the papers published. To decrease this risk, a professional librarian was consulted in the literature search process. We did not include studies focusing only on one specific cancer type, and this also needs to be seen as a limitation. As is common in scoping reviews, the quality of publications was not assessed, and we included papers with a variety of methods in the review. This needs to be recognised in interpreting the results. Thus, this review approach produced more rich data for describing the current state of IPE in cancer care.

Based on the review, research on interprofessional education in the field of cancer care is limited. The need for interprofessional education is well recognised, yet provision and research in this field needs to be increased to enhance quality, person-centred care for people affected by cancer and efficient delivery of cancer care. In the future research would benefit from a more systematic approach to underpinning the theoretical framework on IPE. The evaluation of impact of IPE is currently mainly focused on HCPs perspective. Further research is needed to evaluate the impact on patient care. It is also evident that research and IPE programme development is very limited in the European context and therefore research is needed to strengthen the IPE development in Europe.

Data availability

All data generated or analysed during this study are included in this published article. Clinical Trial Number: N/A.

Abbreviations

Interprofessional Education

Interprofessional Learning

Healthcare Professional

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Acknowledgements

The authors wish to thank the European Commission for funding this study conducted in the context of the INTERACTEUROPE Project (Grant Agreement No. 101056995). This review was conducted in collaboration with European Oncology Nursing Society, European Society of Surgical Oncology and European Society for Radiotherapy and Oncology.

The research leading to these results has received funding from the European Union, EU4Health Programme 2021–2027 as part of Europe’s Beating Cancer Plan under Grant Agreement no. 101056995. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HaDEA. Neither the European Union nor the granting authority can be held responsible for them.

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VS conceived the review and led the preparation of the review plan, design, data collection, analysis and drafting of the manuscript. All authors participated in planning the review protocol. VS, CDRS, ND conducted the data retrieval, data extraction and analysis. All participated in result synthesis and manuscript preparation. All authors read and approved the final manuscript.

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Sulosaari, V., Dodlek, N., Brandl, A. et al. Interprofessional education in cancer care – a scoping review. BMC Med Educ 24 , 767 (2024). https://doi.org/10.1186/s12909-024-05669-8

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In October, the Violence Prevention Research Program at the University of California, Davis, published a report that found nearly 14 percent of those surveyed strongly agreed that there would be a civil war in the United States in the next few years.

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Transporting precious cargo using the body’s own delivery system

Advances ‘get us one step closer to the ultimate goal of targeted biological drug delivery’

Media Information

• Release Date: July 16, 2024

Media Contacts

Win Reynolds

• (413) 461-6314

Journal: Nature Communications

• Delivery systems in body continuously move materials between cells
• Hijacking these systems allowed scientists to improve loading and delivery of therapeutic proteins
• Biophysical principles could be used to enable more cost-effective loading of biological cargo into cell-derived delivery systems
• Engineered molecules loaded up to 240 times more protein than other loading methods

EVANSTON, Ill. --- Each cell in the body has its own unique delivery system that scientists are working on harnessing to move revolutionary biological drugs — molecules like proteins, RNA and combinations of the two — to specific diseased parts of the body.

A new study from Northwestern University hijacked the transit system and sent tiny, virus-sized containers to effectively deliver an engineered protein to its target cell and trigger a change in the cell’s gene expression. The success came from encouraging engineered proteins to move toward a specific cell membrane structure that the researchers found increased a protein’s likelihood of latching onto the container.

Published in July in the journal Nature Communications, the paper contends the novel technique could be generalizable, paving the road for the goal of targeted biological drug delivery.

The study brings researchers a step closer to addressing a major bottleneck for biological medicine development, determining how to protect fragile molecules in the body and ensure they reach the correct diseased cells in a patient without impacting healthy cells.

The research combines work from two labs in Northwestern’s Center for Synthetic Biology : those of biomedical engineer Neha Kamat and chemical and biological engineer Josh Leonard . The Kamat lab has largely focused on the design of synthetic containers and uses biophysical principles to control molecules targeting other cells.  Leonard’s lab develops tools to build these natural delivery containers, termed extracellular vesicles (EVs).

“We were interested in applying some of the biophysical insights that have emerged about how to localize proteins to specific membrane structures so that we could hijack this natural system,” said Kamat, the paper’s co-corresponding author and associate professor at the McCormick School of Engineering. “In this study, we discover general ways to load drug cargo into these vesicles very efficiently while preserving their function. This might enable more effective and affordable extracellular vesicle-based biological medicines.”

The keys to this “cargo loading” approach are sites on cell membranes called lipid rafts. These regions are more structured than the rest of the membrane and reliably contain specific proteins and lipids.

“Lipid rafts are thought by some to play a role in the genesis of EVs, as EV membranes contain the same lipids found in lipid rafts,” said Justin Peruzzi, who co-led the study with Taylor Gunnels as doctoral students in Kamat’s lab. Gunnels’ work in the lab is ongoing, and Peruzzi, who completed his Ph.D., works as a scientist at a protein-based medicine company. “We hypothesized that if we engineered proteins to associate with lipid rafts, they may be loaded into the vesicles, allowing them to be delivered to other cells.”

The team used protein databases and lab experiments to determine that lipid raft-association is an efficient method to load protein cargo into EVs, enabling up to a stunning 240 times more protein to be loaded into vesicles.

After discovering this biophysical principle, the researchers demonstrated a practical application of the method. They engineered cells to produce a protein called a transcription factor, loaded it into EVs and then delivered it to a cell to alter the recipient cell’s gene expression — without compromising the protein’s function upon delivery.

Kamat and Leonard said the main challenge in loading therapeutic cargo into EVs is that the producer cell and the recipient cell are often at odds with each other. In the cell producing the EV, for example, you might engineer therapeutic cargo to associate tightly to a membrane to increase the chance it moves into a soon-to-be released EV. However, this same behavior is often undesirable in a recipient cell because delivered cargo stuck to a membrane might be nonfunctional. Instead, you might want that cargo to release from the EV membrane and move to the cell’s nucleus to perform its biological function. The answer was the creation of cargo with reversible functions.

“Tools that enable reversible membrane association could be really powerful when building EV-based medicines,” said Gunnels. “Although we’re not yet sure of the precise mechanism, we see evidence of this reversibility with our approach. We were able to show that by modulating lipid-protein interactions, we could load and functionally deliver our model therapeutic cargo. Looking forward, we’re eager to use this approach to load therapeutically relevant molecules, like CRISPR gene-editing systems.”

The researchers said they’re eager to try the approach with medicinal cargo for disease applications in immunotherapy and regenerative medicine.

“If we can load functional biomedicines into EVs that are engineered to only deliver those biomolecules to diseased cells, we can open the door to treating all sorts of diseases,” said Leonard, the co-corresponding author and a McCormick professor. “Because of the generalizability that we observed in our system, we think this study's findings could be applied to deliver a wide array of therapeutic cargos for various disease states.”

The paper, titled “Enhancing extracellular vesicle cargo loading and functional delivery by engineering protein-lipid interactions,” was supported by the McCormick Research Catalyst Program at Northwestern University, the National Science Foundation (grants 1844219 and 2145050) and NSF Graduate Research Fellowships (DGE-1842165).