Public involvement in health research systems: a governance framework
Bibliographic record
Abstract
BACKGROUND: Growing interest in public involvement in health research has led to organisational and policy change. Additionally, an emerging body of policy-oriented scholarship has begun to identify the organisational and network arrangements that shape public involvement activity. Such developments suggest the need to clearly conceptualise and characterise public involvement in health research in terms of governance. METHODS: We drew on an established health research system framework to analyse governance functions related to public involvement, adapting scoping review methods to identify evidence from a corpus of journal papers and policy reports. We drew on the logics of aggregation and top down configuration, using a qualitative interpretive approach to combine and link findings from different studies into framework categories. RESULTS: We identified a total of 32 scholarly papers and 13 policy reports (n = 45 included papers) with relevance to governance for public involvement. Included papers were broadly consonant in identifying the need for activity to specify and support public involvement across all four governance functions of stewardship, financing, creating and sustaining resources, and research production and use. However, different visions for public involvement, and the activity required to implement it and achieve impact, were particularly evident with respect to the stewardship function, which seeks to set overall directions for research while addressing the potentially competing demands of a system's many constituents. CONCLUSIONS: A governance perspective has considerable value for public involvement in health research systems, supporting efforts to coordinate and institutionalise the burgeoning public involvement enterprise. Furthermore, it highlights challenges for what is, ultimately, a highly political intervention, suggesting that diverse publics must be both involved within health research systems and enrolled as governors of them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.088 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".