Exploring how patients, carers and members of the public are recruited to advisory boards, groups and panels as partners in public and patient involved health research: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Engaging patients, carers and members of the public in health research has become widely recognised as an important approach for bridging the gap between research, and health and social care by increasing the relevance of research for those who benefit from its findings. Specific approaches to engagement vary, but commonly include advisory boards, groups or patient panels that are active throughout all stages of research. The breadth of and optimal strategies for recruiting patients, carers and members of the public to such boards, groups or panels remains unclear. The objective of this manuscript is to identify the breadth of and optimal strategies used to recruit patients, carers and members of the public to advisory boards, groups or panels, within public and patient involvement (PPI) research. METHODS AND ANALYSIS: , an elaboration on the framework by Arksey and O'Malley. The search strategy was co-developed among the research team, PPI research experts and a faculty librarian. The review will take place between July 2021 and June 2022. In July and August 2021, eight electronic databases, MEDLINE (PubMed), MEDLINE (OVID), Embase, CINAHL, PsychINFO, Scopus, Web of Science and Cochrane Library, will be explored to capture all available literature. Two independent reviewers will screen articles by title and abstract and then at full text based on predetermined criteria. The data will be presented in a tabular format with a narrative summary discussing how the research findings relate to the overarching research question. A thematic analysis will also be completed using qualitative description, identifying key themes and gaps in the literature. ETHICS AND DISSEMINATION: Ethics is not required for this review. We aim to disseminate the information gathered through presentations at academic conferences, peer-reviewed publications and consultations with lay audiences.
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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 | 0.182 | 0.155 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".