Partnering with Patients and the Public in the Clinical Trials Ecosystem: A Decade of Lessons Learned and Shared
Bibliographic record
Abstract
Background: In the health research space, it is becoming more common to see patient and public partners as members of teams and contributing to various initiatives, rather than solely as participants in research. We use the term patient and public engagement (PPE) to describe this approach. While reporting mechanisms such as the Guidance for Reporting Involvement of Patients and the Public, Version 2 (GRIPP2), are being used to share teams’ PPE efforts related to specific research projects, to date we have seen less about how organizations have collaborated with patient and public partners on a broader scale. Clinical Trials Ontario (CTO) is a not-for-profit organization in the clinical trials space that has been set up to improve the environment for trials, rather than conduct trials. We share CTO’s PPE processes, how they have evolved over the past decade, outputs related to this work, and lessons learned. Methods: Patient and public partners and team members from CTO used a co-production model. We have co-developed PPE processes to guide and carry out the work under CTO’s strategic pillar of “Engage.” The Patient and Public Engagement Evaluation Tool has played a role in helping ensure CTO’s approaches to co-production are on track. Feedback and insights provided in the evaluation tool after engagements have resulted in iterative changes to this work. Results: CTO has engaged patient and public partners in its work since its inception in 2014. Gradually this work has evolved based on both needs of CTO and the patient and public partners with whom it collaborates. Originally a small group called the Patient and Public Advisory Group was created which met quarterly in person and provided guidance related to specific products (e.g., CTO’s website about clinical trials, a clinical trial finder). Based on lessons learned with that group and the abilities of people to participate, the group was expanded to become CTO’s College of Lived Experience. The College of Lived Experience includes more perspectives and meets more frequently and predominantly virtually, having been formed just before the COVID-19 pandemic was declared. Working with the College has resulted in: members being embedded in a range of CTO projects, a number of public-facing outputs, and generating a research idea and carrying out a project that resulted in a patient partner co-authored, peer reviewed publication. The College is also available to and has provided input and insights into projects and initiatives outside of CTO. We share lessons learned from organizational perspectives and from those College members who are co-authors of this work. Conclusions: CTO’s work in PPE has resulted in a co-production model that is critical to efforts in CTO’s Engage strategic pillar and beyond. We provide templates and outputs of this co-production work and lessons learned. We hope our work helps other clinical trials organizations include patient and public partners in their operational efforts.
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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 | 0.377 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.015 | 0.054 |
| Scholarly communication | 0.058 | 0.072 |
| Open science | 0.011 | 0.052 |
| Research integrity | 0.017 | 0.046 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".