Better engagement, better evidence: working in partnership with patients, the public, and communities in clinical trials with involvement and good participatory practice
Bibliographic record
Abstract
In May 2022, member states of WHO adopted the World Health Assembly WHA75.8 resolution on strengthening clinical trials to provide high-quality evidence on health interventions and to improve research quality and coordination. The resolution recognises the central role of community stakeholders in the clinical trial ecosystem. This paper aims to take stock of the state of the field and define key actions from stakeholders across the clinical trial ecosystem for systematic engagement of patient, public, and community stakeholders in clinical trials. Upfront, sustained, inclusive, and meaningful engagement with patients, public, and community stakeholders intended to benefit from trial outcomes is crucial for several reasons. First, better engagement ensures that trials are well designed and well implemented by considering the unique perspectives and experiences of those they aim to benefit. Second, better engagement enhances the scientific, ethical, and pragmatic value of trials by improving the acceptability, feasibility, and relevance of trial design, implementation, and outcome dissemination. Lastly, improving engagement fosters trust in science and scientists, strengthens research literacy, and contributes to greater trust in research processes. This trust is particularly important in public health emergencies where the urgency for identifying effective interventions, including new vaccines and medicines, often results in limited engagement. In practice, engagement involves activities throughout the trial lifecycle, including research agenda setting, protocol development, trial conduct, and outcome dissemination. Key stakeholders, such as researchers, funders, research ethics committees, and regulators play crucial roles in enabling and implementing engagement via participatory practices. Despite some key markers of progress, challenges remain, including systemic gaps, limited engagement beyond tokenistic involvement, and structural inequities. Addressing these challenges requires action across the clinical trial ecosystem, including strengthening policies, enhancing funding mechanisms, improving regulatory oversight, advocacy, and education of all stakeholders about engagement, and promoting a strong culture of engagement. Advancing the agenda for engagement can promote trust, ethical research conduct, and improve outcomes and wider uptake of findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.599 | 0.569 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.025 | 0.064 |
| Scholarly communication | 0.058 | 0.057 |
| Open science | 0.011 | 0.108 |
| Research integrity | 0.032 | 0.054 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".