Public involvement and health research system governance: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Interest in public involvement in health research projects has led to increased attention on the coordination of public involvement through research organisations, networks and whole systems. We draw on previous work using the 'health research system' framework to explore organisational actors and stewardship functions relevant to governance for public involvement. METHODS: To inform efforts in Ontario, Canada, to mobilise public involvement across the provincial health research enterprise, we conducted an exploratory, qualitative descriptive study of efforts in two jurisdictions (England, United Kingdom, and Alberta, Canada) where there were active policy efforts to support public involvement, alongside jurisdiction-wide efforts to mobilise health research. Focusing on the efforts of public sector organisations with responsibility for funding health research, enabling public involvement, and using research results, we conducted in-depth, semi-structured interviews with 26 expert informants and used a qualitative thematic approach to explore how the involvement of publics in health research has been embedded and supported. RESULTS: We identified three sets of common issues in efforts to advance public involvement. First, the initial aim to embed public involvement leveraged efforts to build self-conscious research 'systems', and mobilised policy guidance, direction, investment and infrastructure. Second, efforts to sustain public involvement aimed to deepen involvement activity and tackle diversity limitations, while managing the challenges of influencing research priorities and forging common purpose on the evaluation of public involvement. Finally, public involvement was itself an influential force, with the potential to reinforce - or complicate - the ties that link actors within research systems, and to support - or constrain - the research system's capacity to serve and strengthen health systems. CONCLUSIONS: Despite differences in the two jurisdictions analysed and in the organisation of public involvement within them, the supporters and stewards of public involvement sought to leverage research systems to advance public involvement, anticipated similar opportunities for improvement in involvement processes and identified similar challenges for future involvement activities. This suggests the value of a health research system framework in governance for public involvement, and the importance of public involvement for the success of health research systems and the health systems they aim to serve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.127 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".