A scoping review to identify opportunities and challenges for communities of South Asian origin in accessing mental health services and support in high-income countries
Bibliographic record
Abstract
INTRODUCTION: Communities of South Asian origin in high-income countries experience a significantly high prevalence of mental health disorders relative to other migrant populations. Emerging evidence highlights socio-cultural and systemic challenges to mental health care access for culturally diverse communities in high-income English speaking countries; however, there is no comprehensive qualitative evidence regarding the opportunities and challenges faced by these communities. To address this knowledge gap, this scoping review aimed to explore the lived experiences of South Asian communities in accessing mental health care across four selected high-income nations (the United Kingdom, Australia, the United States, and Canada). METHODS: This scoping review followed reporting guidelines for scoping reviews outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. A systematic search was conducted to identify qualitative studies and mixed-methods studies published in English between January 2000 - January 2023 in three electronic databases: PubMed, Web of Science, and Scopus. The search strategy included keywords related to South Asian communities, mental health and access to health care in the chosen high-income countries. Extracted qualitative data was analysed using a thematic analysis. RESULTS: Analysis of the 25 included studies identified interlinked opportunities and challenges informing the ability of South Asian communities to access mental health care. Findings have been structured according to dimensions of accessibility of services: (1) approachability, (2) acceptability, (3) availability and accommodation, (4) affordability, and (5) appropriateness. Our study identified myriad challenges faced by South Asian communities including inflexible accessibility to diverse mental health care, difficult family dynamics, stigma and acculturative issues hindering mental health care usage and poor mental health care experiences informing perceptions of mental health care and re-engagement. CONCLUSION: The research finds that access to mental health care for those from South Asian backgrounds requires a comprehensive and nuanced framework that addresses population-specific barriers, systemic inequalities and socio-cultural factors. Recommendations include increasing cultural safety, facilitating flexible models of care and service delivery in practice, alongside policy and funding reflective of systemic mental health care access issues, alongside implementation research to assess the effectiveness of such recommendations.
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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 | 0.034 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".