Values associated with public involvement in health and social care research: a narrative review
Bibliographic record
Abstract
BACKGROUND: Much has been written about public involvement (PI) in health and social care research, but underpinning values are rarely made explicit despite the potential for these to have significant influence on the practice and assessment of PI. OBJECTIVE: The narrative review reported here is part of a larger MRC-funded study which is producing a framework and related guidance on assessing the impact of PI in health and social care research. The review aimed to identify and characterize the range of values associated with PI that are central elements of the framework. METHODS: We undertook a review and narrative synthesis of diverse literatures of PI in health and social care research, including twenty existing reviews and twenty-four chapters in sixteen textbooks. RESULTS: Three overarching value systems were identified, each containing five value clusters. (i) A system concerned with ethical and/or political issues including value clusters associated with empowerment; change/action; accountability/transparency; rights; and ethics (normative values). (ii). A system concerned with the consequences of public involvement in research including value clusters associated with effectiveness; quality/relevance; validity/reliability; representativeness/objectivity/generalizability; and evidence (substantive values). (iii) A system concerned with the conduct of public involvement in including value clusters associated with Partnership/equality; respect/trust; openness and honesty; independence; and clarity (process values). CONCLUSION: Our review identified three systems associated with PI in health and social care research focused on normative, substantive and process values. The findings suggest that research teams should consider and make explicit the values they attach to PI in research and discuss ways in which potential tensions may be managed in order to maximize the benefits of PI for researchers, lay experts and the research.
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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 | 0.058 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".