Recruitment, retention and reporting of ethnic representativeness in maternity trials: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Black and Asian women experience significantly higher rates of mortality and morbidity perinatally compared with white women and are more likely to lose their babies. These groups are also under-represented in clinical research, resulting in evidence that may not be generalisable. Tools have been developed to facilitate the inclusion of ethnic minority groups, but it is unknown to what extent representation and inclusion are considered in maternity trials. AIM: To provide an overview of how ethnically diverse recruitment is considered and reported in maternity trials in the UK. METHODS: A scoping review was conducted, undertaking a systematic search to identify published trial protocols and their subsequent results papers, conducted within the UK, recruiting women during pregnancy or within 6 weeks postnatally between 2004 and 2024.Data was extracted from protocols on whether representation of participants was considered in the study design and if specific recruitment and retention strategies were planned for ethnic minority groups.Data extracted from results papers identified whether representation of participants was discussed and if recruitment strategies were discussed; these were compared against the protocol. RESULTS: A total of 96 published protocols met the inclusion criteria; 8 mentioned specific recruitment strategies and 5 mentioned specific retention strategies. Only two included both recruitment and retention strategies. The most common strategies included providing different types of language support and adapting interventions to be culturally appropriate. Strategies were not evaluated.67 results papers were available. Ethnicity was reported in 57 papers, with heterogeneity of categories between papers. Only 32 papers discussed representativeness of participants. CONCLUSION: Few maternity trials report considerations on how they ensure they are recruiting and retaining ethnically representative participants. Minimal discussion is undertaken around the extent to which trial participants reflect the population to which findings will be applied.Further work is needed to support implementation and evaluation of inclusive research guidance. Failing to ensure those from ethnic minority groups are included in research can exacerbate inequalities.
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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 | 0.198 | 0.521 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".