Exploring barriers and enablers to primary mental health care among South Asian women in Montreal: A qualitative exploratory study
Bibliographic record
Abstract
Background: In Canadian health surveys, racialized groups and recent immigrants with symptoms of psychological distress report lower rates of being diagnosed and treated compared to their European white counterparts.South Asians are Canada's largest visible minority group, and lower detection may be related to difficulties expressing mental health challenges because it is considered as a taboo subject and still stigmatized for South Asians.When accessing healthcare in Canada, South Asians face the additional difficulties of expressing their mental health challenges in a second language.South Asian women in traditional households face mental health stressors because of gendered roles and expectations and they have limited agency and control over decision-making, including access to healthcare resources.Objectives: This study seeks: 1) to understand the barriers and enablers to expressing mental health challenges and accessing care among South Asian immigrant women from different linguistic groups who receive primary healthcare services in English; and 2) to assess the acceptability and potential uptake of self-help strategies as a first step for managing their mental health challenges in a context of limited availability of psychological services. Methods:The exploratory qualitative study used semi-structured in-depth interviews of a purposive sample of 16 South Asian immigrant women from Bangladesh, Pakistan, India, and Sri Lanka.Participants with direct or indirect experience with mental health conditions were referred from a family medicine teaching clinic serving a multicultural neighborhood.Others were recruited from a South Asian women's community centre in Montreal, Quebec.Data analysis involved an inductive-deductive thematic analysis approach, supported by the framework method.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".