Workshops

Clinical practice guidelines frequently rely on thresholds and targets (risk cut-offs, surrogate outcomes, and treatment goals) to translate complex evidence into actionable recommendations. These “lines in the sand” enhance clarity and usability but can oversimplify uncertainty, embed implicit value judgments, and misalign recommendations with patient-important outcomes. As guidelines increasingly influence clinical practice, quality metrics, and policy, there is a growing need to critically examine how thresholds and targets are constructed, interpreted, and applied.

This workshop aims to develop participants’ ability to critically evaluate threshold- and target-based recommendations, and explore considerations for their development, interpretation, and application to clinical practice. Specifically, participants will:

• Examine three common approaches:

  • Risk thresholds for prevention (e.g., cardiovascular and fracture risk cut-offs).
  • Surrogate targets with weak or inconsistent links to hard outcomes (e.g., glycemic or lipid targets).
  • Surrogate targets with demonstrated benefits tempered by meaningful harms (e.g., intensive blood pressure targets).
• Identify methodological, clinical, and ethical challenges inherent in each approach.
• Apply practical strategies to avoid pitfalls in guideline development, interpretation, and clinical translation.

The workshop will use an interactive, case-based format combining brief didactic segments with structured group exercises. Participants will work with real-world guideline excerpts and simplified evidence summaries to explore how different threshold and target choices shape clinical recommendations and how their development shapes interpretation, translation, and clinician expectations.

The program will be organized into three modules aligned with three core “line in the sand” approaches:

• Risk Thresholds for Prevention
  Participants will explore continuous risk distributions and examine how different threshold choices alter treatment eligibility, perceived benefit, and equity. Discussion will focus on the assumptions underlying threshold selection and approaches to communicating graded risk rather than binary decisions.
• Surrogate Targets with Weak Links to Hard Outcomes
  Participants will evaluate common surrogate outcomes using criteria for validity and patient relevance and will examine how reliance on weak surrogates can lead to overestimation of benefit and overtreatment. Strategies for prioritizing patient-important outcomes in guideline recommendations will be discussed.
• Surrogate Targets with Demonstrated Benefits but Meaningful Harms
  Participants will analyze benefit–harm trade-offs associated with lower treatment targets, including heterogeneity of treatment effects and patient vulnerability to harm and burden of care, exploring the presentation of targets as rigid numbers, as ranges, and as options in context of overall care.

Facilitated discussion will synthesize insights across modules and introduce a practical framework for evaluating thresholds and targets in guideline development. Participants will leave with a set of conceptual tools and practical heuristics to support more transparent, nuanced, and patient-centred guideline recommendations.

GRADE guidance has become disorganized and excessively complicated. Core GRADE addresses these problems by providing a seven-article series in the BMJ that provides all systematic reviewers and guideline developers need to address paired comparisons of alternative management strategies.

To familiarize participants with Core GRADE.

Serial single-concept short presentations will review key Core GRADE concepts. A small group exercise carried out in buzz groups (3 to 5 individuals per group) will follow each of these presentations. The concepts covered will include complexities that Core GRADE abjures and the alternatives it provides; addressing risk of bias and certainty as continua; using algorithms to work through rating down decisions; dealing with a mix of high and low risk of bias studies; addressing possible subgroup issues in question formulation; clarifying inconsistency versus indirectness; and choosing the target of certainty rating.

Our learners are increasingly exploring the potential of AI to support their studies, using it to take notes, guide formative learning, draft assessments, and simulate clinical scenarios. This growing use offers valuable opportunities to enhance evidence-based healthcare (EBHC) education while also prompting important discussions about reliability, value, and ethics. In this session, we will consider AI as a useful and evolving tool for both learners and educators. We will share practical strategies for teaching its ethical and effective use, developing AI literacy, and applying AI thoughtfully to common EBHC teaching and learning activities.

By the end of this session, participants will be able to:

• Describe multiple uses for AI in EBHC;
• Describe foundational AI concepts teachers and learners need to know to appropriately use AI for EBHC;
• Compare and contrast several AI platforms to support EBHC;
• Develop a learning experience that uses AI to support EBHC.

Workshop leaders will present their experiences and lessons learned teaching learners to use AI to support EBHC. Leaders will prepare a virtual handout with links to AI platforms for participants to use during and after the session. (15 min)
Leaders will challenge participants to use their preferred AI platform (or try a new platform) to answer a clinical question posed by the workshop leaders. (5 min to search, 10 min for large group debrief)
Workshop leaders will share best practices in using AI during learning experiences to support EBHC. (10 min)
Participants will work in pairs or small groups to design a learning experience that they can implement at their home institution to use AI to support EBHC. (10 min for small group work, 10 min for large group share out)

For more than 30 years, clinical health practice has been guided by the principles of evidence-based practice (EBP), where decisions are informed by the best available research, clinical expertise, patient preferences, and contextual factors. This approach has become not only important but, in many contexts, essential to ensuring high quality care. More recently, similar expectations have extended to the research process itself, emphasizing the need for new studies to build on existing evidence and avoid research waste.

However, the same evidence-based mindset is equally relevant and valuable for teaching in health care education. We propose that an evidence-based approach to teaching mirrors the logic of clinical EBP by integrating: (a) educational research and theory; (b) the educators’ professional experience; (c) student’s perspectives, needs and learning preferences; and (d) contextual factors, such as curriculum, resources, and learning environment. Such an approach supports deliberate choices about both teaching content and pedagogy and promotes constructive alignment between learning outcomes, teaching activities, and assessment.

This workshop explores whether and how evidence-based principles can meaningfully shape teaching practice in healthcare education, and how educators can use this approach to improve the learning experience of their students.

To present, critically discuss, and collaboratively refine the evidence-based approach to the design and delivery of teaching in health care education.

The interactive workshop invites participants to critically discuss and collaboratively refine a proposal for how to apply an evidence-based approach to teaching health care students. Drawing on principles from EBP, the session will explore how such an approach can strengthen the planning, delivery, and evaluation of teaching in health care education.

The workshop consists of three parts:

• A brief introduction to the concept of the evidence-based approach to teaching;
• Small-group discussions where participants apply, question, and critique the EBP approach to teaching;
• A plenary synthesis, where participants identify insights, barriers, opportunities, and potential next steps.

Emphasis will be placed on reflection, debate, and the co-creation of practical strategies. Participants will leave with a clearer understanding of how an evidence-based approach can meaningfully guide and improve their own teaching practice.

Health information must not only be accurate and evidence-based, it must also be culturally and contextually relevant for addressing the diverse needs of different populations. Generative AI (such as ChatGPT, Claude, Gemini and Copilot) is emerging as a valuable tool for communicating health information, making information more accessible to the public. However, researchers and communicators need practical strategies to use this technology ethically and effectively across diverse audiences, while ensuring content remains clear, relatable, inclusive and accurate. This workshop addresses this need at a time when both the use of AI and inclusive science communication are growing in recognition and importance across the health sector.

This workshop aims to equip participants with foundational knowledge and practical skills to use Generative AI to assist with accurate and equity-centred communication of evidence-based health information for diverse communities.

Learning objectives
By the end of the workshop, participants will be able to:

• identify key principles of inclusive science communication and recognise the challenges of AI-generated outputs
• apply effective prompt engineering techniques—including persona, contextual, and iterative prompting—to tailor health messages
• integrate established frameworks into their communication strategies
• critically evaluate AI outputs for accuracy, cultural relevance, and inclusion, adapting messages as needed
• engage in peer review to refine and optimise their communication approaches in real-time

Workshop outline
Using a hands-on and participatory approach, the workshop will integrate principles of inclusive science communication, and prioritise equity, diversity, and inclusion. A structured progression of activities will begin with an exploration of both the potential and challenges of using Generative AI to assist with communicating health information, followed by practical, hands-on exercises based on real-world scenarios.

Introduction to key frameworks, principles, tips and tricks
Participants will be introduced to inclusive science communication frameworks and principles, science communication tips and tricks, prompt engineering techniques, and an overview of Generative AI, including pitfalls and practices.

Hands-on exercises
Demonstrations on how to craft different types of prompts (including persona and contextual) and iterative prompting will prepare participants for a series of practical exercises based on real-world scenarios.

Participants will use supplied evidence-based healthcare information (or can supply their own research paper) to craft prompts. This process is iterative, allowing participants the time to learn and practice generating and refining AI-generated health information that is inclusive and accurate.

Collaborative learning
Participants will engage in collaborative learning by sharing their prompts and AI-generated outputs for peer feedback, fostering a dynamic and supportive learning environment. Strategies for evaluating and refining AI-generated outputs will be discussed to ensure relevance, accuracy, and inclusivity for specific interest-holders.

The session will conclude with an open discussion and Q&A, allowing attendees to reflect on key takeaways and explore future applications of Generative AI for inclusive health communication, for example, multilingual support and health literacy adaptation.

Rapid reviews (RRs), conducted by streamlining the systematic review process, are an increasingly popular mode of evidence synthesis. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline was designed to improve systematic review reporting, and has been extended to address various types and modes of evidence syntheses. However, a PRISMA extension for RRs (PRISMA-RR) has not been developed. Poor RR reporting quality and methodological transparency has been identified in the literature, underlining the need for clear RR reporting guidance. Complete and transparent reporting of RRs is critical to enhancing interest holder understanding of the work conducted, promoting better documentation of the RR process to better support decision-making. This project was guided by a steering committee consisting of international experts in systematic reviews, RRs, and health reporting guidelines, and one patient partner with extensive experience in patient engagement in systematic reviews and RRs. In accordance with JBI scoping review methodology and reporting guidance for scoping reviews (PRISMA-ScR), we conducted a scoping review of the literature to identify studies that evaluated the reporting completeness or quality of RRs. A comprehensive search strategy was applied across MEDLINE, Cochrane Methodology Register, EMBASE and ERIC on in June 2024. Eligible studies employed any research design and addressed topics related to human health and/or philosophical inquiry. To ensure methodological rigor, study selection was conducted independently in pairs, and data extraction was undertaken with one extractor and one verifier. Extracted information was grouped into high-level domains aligned with PRISMA 2020. The steering committee assessed all candidate concepts and eliminated those that lacked specificity to rapid reviews or overlapped with PRISMA 2020 items. These resulting concepts, which were drafted to inform the subsequent Delphi study, will be rated for inclusion in PRISMA-RR by content experts, researchers, methodologists, policy and decision-makers, clinicians, journal editors, patients, and members of the public using a Delphi survey with a five-point scale. The results of the Delphi process will be brought forward to a group of key interest holders in a consensus meeting to determine placement in the final tool and elaboration manuscript.

The goal of this workshop is to pilot the newly developed PRISMA-RR guideline that reflects current evidence, incorporates the perspectives of patients and the public, and is aligned with PRISMA 2020.

During this one-hour workshop, Dr. Andrea Tricco will present and pilot the PRISMA-RR checklist using an example from the published literature. Workshop participants will include rapid review authors, policy and decision-makers, students, patients, and other experts in the field of evidence synthesis and rapid reviews. These participants will apply the checklist to the example publication and provide feedback on ease-of-use, including wording, structure, and clarity. The results of this workshop will be used to refine the checklist prior to publication.

Knowledge brokers play pivotal roles in the implementation of evidence-informed decision making (EIDM) in organizations by engaging and supporting teams to build capacity in EIDM and facilitating organizational culture change. To do this knowledge brokers require a range of knowledge and skills related to:

• knowledge management (e.g., obtaining relevant evidence, creating tailored knowledge products, supporting evidence sharing);
• knowledge linkage and exchange (e.g., facilitating collaboration, developing and maintaining networks);
• and capacity building (e.g., helping develop analytic skills, facilitating and evaluating change).

While the role has been shown to be pivotal in facilitating EIDM, how to obtain the knowledge and skills required to act as one is not clearly defined.

The aims of this workshop are to:

• Use real-world examples to facilitate sharing among participants their knowledge, expertise and experiences in knowledge broker/intermediary roles;
• Provide opportunities for participants to reflect on personal characteristics that support success in such roles;
• Identify available resources that support capacity development as a knowledge broker;
• Summarize in one document the tips, strategies and capacity development resources compiled during the workshop to share with participants following the conference.

The workshop will consist of large group didactic and small group interactive components including:

• Introduction: (5 min)
  Introduce facilitators, workshop structure and objectives, define knowledge broker/intermediary roles.
• Small group activity: Real world examples (20 min)
  Participants discuss the approaches and strategies they would implement to address the real-world examples. Each table will select a notetaker to keep track of identified approaches.
• Debrief with large group: (10 min)
  Participants will discuss key strategies discussed at their table. Notes from each table to be submitted to facilitator.
• Small group activity: Characteristics needed for the role, capacity development resource (15 min)
  Participants will identify the most important characteristics that they associate with success in the role, and resources they are aware of that support capacity development. Each table will select a notetaker to keep track of characteristics and resources to be shared with the faciliator.
• Debrief with large group and wrap up: (10 min)
  Participants discuss what advice/guidance they would give to someone starting new in this role and favorite resources they recommend

Wrap-up and next steps for compiling all the data into a resource document.

The Evidence-Based Research (EBR) movement emphasizes that those conducting primary research studies should take into consideration all the already available evidence till that point in a systematically and transparent manner. This has great potential to increase research value and reduce redundancy and waste.

This Workshop explores the concept of EBR, its contribution towards making evidence (including evidence syntheses and guidelines) more trustworthy and efficient; modalities to implement EBR; and the global efforts of the Evidence-Based Research Network (EBRNetwork).

Format: Interactive workshop with four short Presentations (10 minutes each) addressing the objectives (stated above) in diverse healthcare and non-healthcare settings, followed by a Moderated Discussion amongst the facilitators and participants (45 minutes), and Conclusion (5 minutes).

Presentation topics (4 presentations of 10 minutes each):

• What? This will outline the concept of EBR.
• Why? This will explore how EBR contributes to building trustworthy evidence efficiently and economically.
• How? This will outline potential approaches for implementing EBR in diverse settings.
• Who? This will explore the efforts of the EBRNetwork to foster global equity in EBR.

Moderated discussion (45 minutes): Workshop participants will:

• share their perceptions and experiences of EBR in various settings;
• identify challenges in implementing the approaches presented by the facilitators;
• discuss empowerment of individuals and institutions to foster equity in EBR.

Conclusion (5 minutes): Wrap-up and next steps.

Activities/Interaction Plans: The Workshop is designed to be interactive. Over 50% of the time is allocated for discussion, participant engagement, and mutual sharing.

AI-generated text is now routinely used to draft summaries, explain research findings, and translate technical material for broader audiences. While these systems can produce fluent and persuasive outputs, fluency should not be mistaken for evidentiary quality. In science communication, credibility depends on accuracy, transparency, contextual sensitivity, and responsible representation of uncertainty.

AI systems generate content by predicting linguistic patterns rather than verifying claims against established research. This creates identifiable risks: overstatement of findings, omission of limitations, compression of nuance, and reproduction of bias. These distortions are often subtle and embedded within otherwise coherent text. The professional challenge is therefore not whether to use AI, but how to exercise structured oversight. Evidence-based communication requires explicit standards, clear instruction design, and systematic evaluation of outputs. This workshop approaches AI-assisted communication as a matter of methodological governance and professional accountability.

This workshop aims to:

• Position AI systems as epistemic tools that shape how evidence is represented.
• Strengthen participants’ ability to apply evidence standards when generating and reviewing AI-assisted communication.
• Develop competency in drafting structured instruction specifications that reduce ambiguity and improve evidentiary alignment.
• Build analytical skills to diagnose weaknesses in reasoning, framing, uncertainty representation, and bias.
• Support integration of AI governance principles into curricula, research communication practice, and institutional policy.

• Conceptual Framing: AI as a Knowledge-Mediating System (20 minutes)
  The session opens by examining how AI systems construct responses and where distortions may arise. Participants explore key concepts relevant to evidence-based communication, including:
  • Hierarchies of evidence and source reliability
  • Transparency and traceability
  • Representation of uncertainty
  • Framing effects in knowledge translation
  • Structural bias in language systems
  Rather than debating whether AI is “good” or “bad,” this segment establishes a conceptual model for analysing AI outputs as constructed artefacts that require scrutiny.
• Instruction Specification as Professional Practice (25 minutes)
  This segment reframes prompting as drafting structured instruction specifications. Participants examine how clarity of task definition shapes output quality.
  Focus areas include:
  • Defining evidentiary expectations explicitly
  • Constraining interpretive drift
  • Specifying audience parameters
  • Managing scope and complexity
  • Embedding transparency requirements
  Live demonstrations illustrate how small changes in instruction design alter reasoning depth, inclusivity, and clarity. Participants practice constructing specifications that reflect professional standards rather than informal experimentation.
• Analytical Review Lab: Diagnosing AI Outputs (25 minutes)
  Participants work with realistic science communication scenarios, such as summarising research findings or explaining technical evidence to non-specialist audiences.
  Using a structured diagnostic framework, they analyse AI-generated outputs across core dimensions:
  • Evidentiary grounding
  • Logical coherence
  • Representation of uncertainty
  • Framing and emphasis
  • Inclusivity and implicit bias
  The focus is on identifying structural weaknesses in argumentation and interpretation. Participants learn to move beyond editing language toward evaluating reasoning quality.
• Iterative Reconstruction and Peer Analysis (15 minutes)
  Participants refine their instruction specifications and regenerate outputs. They compare iterations to observe how changes influence evidentiary alignment and clarity.
  Peer analysis centres on explaining why revisions improved the output. This reinforces professional judgement and methodological awareness rather than surface correction.
• Institutional Integration and Governance (5–10 minutes)
  The final segment shifts from individual skill to institutional application. Participants consider how to:
  • Embed AI oversight into research communication workflows
  • Incorporate critical AI literacy into curricula
  • Develop internal guidance aligned with research integrity standards
  • Align AI use with ethical and inclusivity principles

Systematic reviews and other forms of evidence syntheses are foundational to evidence-based health care (EBHC), summarizing and guiding future research. However, the purpose and meriting value of different types of evidence syntheses are inconsistently understood and applied within the academic context. Academic policies governing if and how evidence syntheses may be used within doctoral theses and for academic promotion vary across institutions and countries in terms of content requirements and potential restrictions. In our experience, current policies are also sometimes unclear both to those seeking to demonstrate merit and to those responsible for assessing it. Consequently, clarifying and harmonizing the role of evidence syntheses within the academic context could serve as a foundation for increasing awareness and strengthening the implementation of this study design.

The overarching goal of this workshop is to serve as an initial step towards developing international consensus on policy guidance for evidence synthesis in the academic context. It aims to clarify and harmonize the role of this study design―including systematic reviews and other types of evidence syntheses―grounded in the expertise of the EBHC community.

Through individual Mentimeter feedback, small‑group discussions, and large‑group reflections, participants will identify and explore the value of evidence synthesis and core components of policy guidance for the academic context―including scope, definitions, differentiation from other study designs, and standards or recommendations for conduct and reporting―and will be invited to further collaboration on the topic.

0–10 min: Welcome and framing

• Introductions (leaders and potentially participants, depending on the number of participants)
• Workshop purpose, expected outputs, and how results will be captured through polls and summaries
• Count number of participants, take a picture for future reference

 

10–30 min: Background and context-setting

• Introduction to the value of evidence synthesis for PhD students and researchers
• Brief overview of variation in policies for PhD thesis and promotion to associate professor across institutions and countries, including examples from Swedish universities
  • Ringsten et al., Cochrane Evid Synth Methods 2025;3(1):e70015; doi.org/10.1002/cesm.70069
  • Ringsten et al. Cochrane Evid Synth Methods 2026;4(1):e70069) doi.org/10.1002/cesm.70015
• Mentimeter of pros and cons of establishing international consensus on policy guidance for evidence synthesis in the academic context

30–60 min: Small-group discussions (guided by facilitators if available)

• Explain discussion prompts, roles (facilitator/note-taker/timekeeper), and outputs
• Form small groups (4–6 participants per group)
• Group work based on the key prompts listed below, supported by accompanying examples (illustrating extremes of policy content; if time is limited, some prompts will be omitted):
  • Value: The value of evidence synthesis in academic contexts, agree or disagree with statements
  • Purpose of policies: Ideal (high bar) vs pragmatic (implementable) approaches in policies, or both
  • Scope of policies: Systematic reviews only vs all evidence synthesis types
  • Differentiation in policies: Do different types of evidence synthesis require separate policies, or not?
  • Relation to other study designs: Are evidence syntheses required to be distinguished from other study designs, or not?
  • Criteria vs guidance: Is a strict minimum standard, or set of recommendations, for conduct and reporting required or not?
  • Standards: If a standard is required, which standards should be used?
  • Merit value: Are restrictions on the use of evidence syntheses in academic meriting warranted or desirable, or not?
  • Implementation: Is clarification required for those seeking to demonstrate merit and for those responsible for assessing it, or not?
  • Context: Are differing policies across contexts (such as certain fields, universities and countries) required, or not?
• To conclude, each group reports free-text comments from their discussion on key prompts into Mentimeter

60–80 min: Large-group reflections and synthesis

• Each key prompt is discussed separately, with comments from all groups visualised in Mentimeter
• The session concludes with a Mentimeter activity in which participants provide individual votes on the identified pros and cons of establishing international consensus on policy guidance for evidence synthesis in the academic context, as well as on each key prompt discussed.

80–90 min: Next steps and closing

• Invite collaborators for a follow-up working group by adding names to the digital survey for future contact.
• Agree on immediate next steps (e.g., shared notes, follow-up meeting, drafting plan).
• Closing remarks, including suggestions for continued discussion and progress beyond the meeting, and thanks to all participants for joining the workshop.

The 2018 JAMA Network Open consensus statement on core EBHC competencies (Albarqouni et al. 2018) is widely cited. However, little is known about whether and how these competencies have been translated into teaching practices, for example through curriculum mapping, alignment with learning outcomes, and integration into EBP teaching activities. Understanding how educators interpret, prioritise and embed these competencies is essential to judge the statement’s intended influence on health professional education and whether learners are being adequately prepared for evidence-based decision-making.

A pre-conference citation analysis conducted by the workshop facilitators provides new insight into patterns of use, gaps, and potential opportunities; this workshop allows participants to interpret these findings together and reflect on how the core competencies are used in their own programmes.

• Share key findings from a citation analysis.
• Enable participants to reflect on and benchmark their own teaching practice.
• Co-create actionable recommendations to strengthen teaching and implementation of EBHC competencies internationally.

60 minutes program:

• Introduction to speakers and workshop program (5 min);
• Presentation of citation analysis findings (15 min);
• Small-group reflection (20 min);
• Collective prioritization (10 min);
• Synthesise & next steps (10 min).

Outputs:
3–5 prioritised recommendations and anonymised group artefacts (flip-charts) captured as photos. Participants will be invited to collaborate on a subsequent manuscript; authorship will be offered to those who meet established international authorship criteria.