Barriers and facilitators to recruitment of South Asians to health research: a scoping review
Bibliographic record
Abstract
OBJECTIVES: People of South Asian ethnicity are under-represented in health research studies. The objectives of this scoping review were to examine the barriers and facilitators to recruitment of South Asians to health research studies and to describe strategies for improving recruitment. DESIGN: Scoping review METHODS: Using the Arksey and O'Malley framework for scoping reviews, we comprehensively searched electronic databases (MEDLINE via PubMed, Cochrane Library, CINAHL and PsycINFO). Studies that identified barriers and facilitators to recruitment, or recruitment strategies for South Asian populations were included. Recruitment barriers, facilitators and strategies were grouped thematically and summarised narratively. SYNTHESIS: Of 1846 potentially relevant articles, 15 met the inclusion criteria and were included in the thematic synthesis. Multiple facilitators and barriers to enrolment of South Asians in health research studies were identified; these most commonly related to logistical challenges, language and cultural barriers, concerns about adverse consequences of participating and mistrust of research. Several actionable strategies were discussed, the most common being engagement of South Asian communities, demonstration of cultural competency, provision of incentives and benefits, language sensitivity through the use of translators and translated materials and the development of trust and personal relationships. CONCLUSION: There is a growing awareness of the barriers and facilitators to recruitment of South Asian participants to health research studies. Knowledge of effective recruitment strategies and implementation during the grant funding stages may reduce the risk of poor recruitment and representation of South Asians.
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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 | 0.146 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".