Frameworks for supporting patient and public involvement in research: Systematic review and co‐design pilot
Bibliographic record
Abstract
BACKGROUND: Numerous frameworks for supporting, evaluating and reporting patient and public involvement in research exist. The literature is diverse and theoretically heterogeneous. OBJECTIVES: To identify and synthesize published frameworks, consider whether and how these have been used, and apply design principles to improve usability. SEARCH STRATEGY: Keyword search of six databases; hand search of eight journals; ancestry and snowball search; requests to experts. INCLUSION CRITERIA: Published, systematic approaches (frameworks) designed to support, evaluate or report on patient or public involvement in health-related research. DATA EXTRACTION AND SYNTHESIS: Data were extracted on provenance; collaborators and sponsors; theoretical basis; lay input; intended user(s) and use(s); topics covered; examples of use; critiques; and updates. We used the Canadian Centre for Excellence on Partnerships with Patients and Public (CEPPP) evaluation tool and hermeneutic methodology to grade and synthesize the frameworks. In five co-design workshops, we tested evidence-based resources based on the review findings. RESULTS: Our final data set consisted of 65 frameworks, most of which scored highly on the CEPPP tool. They had different provenances, intended purposes, strengths and limitations. We grouped them into five categories: power-focused; priority-setting; study-focused; report-focused; and partnership-focused. Frameworks were used mainly by the groups who developed them. The empirical component of our study generated a structured format and evidence-based facilitator notes for a "build your own framework" co-design workshop. CONCLUSION: The plethora of frameworks combined with evidence of limited transferability suggests that a single, off-the-shelf framework may be less useful than a menu of evidence-based resources which stakeholders can use to co-design their own frameworks.
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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 | 0.687 | 0.746 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.051 | 0.048 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".