Explanatory models of common mental disorders among South Asians in high-income countries: A systematic review
Bibliographic record
Abstract
Mental health service use by individuals of South Asian origin living outside of South Asia is influenced by cultural factors such as endorsing psycho-social-spiritual over biological explanations, somatisation, and stigma. The aim of this review is to synthesise the evidence about (a) explanatory models of common mental disorders (CMDs) among people of South Asian origin residing in high-income countries, and (b) their help-seeking for CMDs, including formal and informal care. The systematic review protocol was registered a priori on Prospero (registration number CRD42021287583). We ran extensive searches on explanatory models and help-seeking of people of South Asian origin across five databases (MEDLINE, Embase, Cumulated Index to Nursing and Allied Health (CINAHL), PsycINFO, and Global Health). We extracted the data and conducted a narrative synthesis. We included 33 reports and 29 studies (9,030 participants). The participants in the included studies viewed CMDs through a psychosocial rather than a biological lens (e.g., resulting from family issues vs. neurotransmitters). Causal attributions included life stressors and attitudinal and religious/spiritual factors. Commonly used help-seeking strategies included private coping (i.e., crying or praying), speaking to friends and family, and visiting their General Practitioner. We can conclude that cultural factors play an important role in how South Asian individuals experience and understand CMDs. To cope, they use pluralistic help-seeking strategies. Implications for clinical practice and policy include increasing research on the explanatory models of CMDs, involving family in services, and developing community-based interventions for individuals who do not engage with formal 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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".