Understanding Immigrants' Reluctance to Use Mental Health Services: A Qualitative Study from Montreal
Bibliographic record
Abstract
OBJECTIVE: Studies suggest that non-European immigrants to Canada tend to under use mental health services, compared with Canadian-born people. Social, cultural, religious, linguistic, geographic, and economic variables may contribute to this underuse. This paper explores the reasons for underuse of conventional mental health services in a community sample of immigrants with identified emotional and somatic symptoms. METHOD: Fifteen West Indian immigrants in Montreal with somatic symptoms and (or) emotional distress, not currently using mental health services, participated in a face-to-face in-depth interview exploring health care use. Interviews were analyzed thematically to discern common factors explaining reluctance to use services. RESULTS: Across participants' narratives, we identified 3 significant factors explaining their reluctance to use mental health services. First, there was a perceived overwillingness of doctors to rely on pharmaceutical medications as interventions. Second, participants perceived a dismissive attitude and lack of time from physicians in previous encounters that deterred their use of current health service. Third, many participants reported a belief in the curative power of nonmedical interventions, most notably God and to a lesser extent, traditional folk medicine. CONCLUSION: The above factors may highlight important areas for intervention to reduce disparities in immigrant use of mental health care. We present our framework as a model, grounded in empirical data, that further research can explore.
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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 | 0.007 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".