Recruitment, retention and reporting of variables related to ethnic diversity in randomised controlled trials: an umbrella review
Bibliographic record
Abstract
OBJECTIVE: This umbrella review synthesises evidence on the methods used to recruit and retain ethnically diverse participants and report and analyse variables related to ethnic diversity in randomised controlled trials. DESIGN: Umbrella review. DATA SOURCES: Ovid MEDLINE, Ovid Embase, CINAHL, PsycINFO and Cochrane and Campbell Libraries for review papers published between 1 January 2010 and 13 May 2024. ELIGIBILITY CRITERIA: English language systematic reviews focusing on inclusion and reporting of ethnicity variables. Methodological quality was assessed using the AMSTAR 2 tool. RESULTS: Sixty-two systematic reviews were included. Findings point to limited representation and reporting of ethnic diversity in trials. Recruitment strategies commonly reported by the reviews were community engagement, advertisement, face-to-face recruitment, cultural targeting, clinical referral, community presentation, use of technology, incentives and research partnership with communities. Retention strategies highlighted by the reviews included frequent follow-ups on participants to check how they are doing in the study, provision of incentives, use of tailored approaches and culturally appropriate interventions. The findings point to a limited focus on the analysis of variables relevant to ethnic diversity in trials even when they are reported in trials. CONCLUSION: Significant improvements are required in enhancing the recruitment and retention of ethnically diverse participants in trials as well as analysis and reporting of variables relating to diversity in clinical trials. PROSPERO REGISTRATION NUMBER: CRD42022325241.
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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 | 0.302 | 0.617 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".