Taking culture seriously: Ethnolinguistic community perspectives on mental health.
Bibliographic record
Abstract
OBJECTIVE: Ethnolinguistic communities are underserved by mental health systems in immigrant-receiving, multicultural societies, but their perspectives are seldom elicited in mental health research or reform planning. This article helps fill this gap by presenting community perspectives on concepts of mental health, mental illness and mental health experiences with five ethnocultural communities (Latin American, Mandarin-speaking Chinese, Polish, Punjabi Sikh and Somali) in Ontario, Canada. METHODS: Data were collected from 21 focus groups as part of a large-scale, participatory action research project called Taking Culture Seriously in Community Mental Health. RESULTS: The analysis focuses on how mental health and mental illnesses are described, how mental health care is experienced and what recommendations community members provide to improve the mental health system. CONCLUSIONS: Study findings illustrate the importance of the social context of immigration and settlement in conceptualizing mental health and mental distress. We conclude that systemic changes are needed to formulate collaborative, community-based strategies for mental health promotion and interventions.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.032 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".