40 Identifying barriers and enablers to cardiac rehabilitation in South Asian populations: a systematic review of global evidence
Bibliographic record
Abstract
Background South Asian service users living outside the Indian sub-continent are under-represented in cardiac rehabilitation (CR) despite experiencing elevated cardiac-related morality and hospitalisations compared to White Europeans. Factors influencing CR participation are often reported by grouping multiple ethnicities together. To understand cultural differences between ethnicities, and their potential impact on CR participation, barriers and enablers should be reported for individual ethnic populations. Aim To review global evidence identifying barriers and enablers to CR reported by South Asian ethnic minorities, living outside Indian subcontinent. Methods A systematic review of 6 databases (MEDLINE (OVID including PubMed), CINAHL APA PsycINFO, Cochrane (CENTRAL), Scopus and Web of Science) was carried out in January 2024, with no language or date limitations. Global primary research from countries outside the Indian subcontinent were included. Barriers and enablers to CR participation were extracted. Thematic analysis with narrative synthesis was conducted. Results 13 studies met inclusion criteria (n=10 UK, n=3 Canada) featuring n=384 South Asian service users. Five key themes were generated: 1. communication and knowledge, 2. culture, motivation and behaviours 3. religion, 4. programme delivery, 5. practical considerations (figure 1). Language barriers, family support, fatalistic beliefs and motivation were key to CR participation. Some barriers overlapped with those for White European, however they are more impactful to South Asian service users due to different social determinants of health. Conclusion Barriers and enablers to CR for South Asian ethnic minorities are complex and multi-dimensional. Not all are resultant of ethnicity, however prominent barriers and enablers for South Asian service users are inextricably linked to their cultural and societal norms. By identifying specific ethnic barriers and enablers, services can better understand cultural influences relating to South Asian CR participation. Funder Statement: This is independent research funded by the Wellcome Trust and carried out at the National Institute for Health and Care Research (NIHR) Leicester Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the Wellcome Trust, the NIHR or the Department of Health and Social 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 | 0.024 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".