Recruitment of patients, carers and members of the public to advisory boards, groups and panels in public and patient involved health research: a scoping review
Bibliographic record
Abstract
OBJECTIVES: The objectives of this scoping review are to: (1) identify the distribution of and context of the recruitment strategies used, (2) explore the facilitators, benefits, barriers and ethical issues of the identified recruitment strategies, (3) distinguish the varying terminology for involvement (ie, panels, boards, individual) and (4) determine if the individual recruitment strategies used were to address issues of representation or bias. DESIGN: A scoping review. SETTING: . Seven electronic databases were explored including Scopus, Medline, PubMed, Web of Science, CINAHL, Cochrane Library and PsycINFO (conducted July 2021). The search strategy was codeveloped among the research team, PPI research experts and a faculty librarian. Two independent reviewers screened articles by title and abstract and then at full text based on predetermined criteria. PRIMARY AND SECONDARY OUTCOME MEASURES: Explore recruitment strategies used, facilitators, benefits, barriers and ethical issues of the identified recruitment strategies. Identify terminology for involvement. Explore recruitment strategies used to address issues of representation or bias. RESULTS: The final sample was from 51 sources. A large portion of the extracted empirical literature had a clinical focus (37%, n=13) but was not a randomised control trial. The most common recruitment strategies used were human networks (78%, n=40), such as word of mouth, foundation affiliation, existing networks, clinics or personal contacts. Within the reviewed literature, there was a lack of discussion pertaining to facilitators, benefits, barriers and ethical considerations of recruitment strategies was apparent. Finally, 41% (n=21) of studies employed or proposed recruitment strategies or considerations to address issues of representation or bias. CONCLUSION: We conclude with four key recommendations that researchers can use to better understand appropriate routes to meaningfully involve patients, carers and members of the public to cocreate the evidence informing their care.
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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 | 0.204 | 0.466 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".