The mental health status of ethnocultural minorities in Ontario and their mental health care
Bibliographic record
Abstract
BACKGROUND: Mental disorders are a leading cause of disability and early mortality. The objective of this study was to describe and compare psychosocial indicators and mental health service use among ethnoculturally-diverse Ontarians. METHODS: This is a cross-sectional analysis of the Ontario Health Study pilot investigation. Residents were mailed an invitation to one of 3 assessment centres (urban, rural and northern sites) from March 2009 to July 2010. Participants had an interview with a nurse and completed a questionnaire on a touchscreen kiosk. The questionnaire included sociodemographic items, and scales assessing symptoms of depressive symptoms (CES-D) and anxiety (GAD-7), social support (Lubben Social Network Scale), stressful life events, and mental health service use. RESULTS: Eight thousand two hundred thirty-five residents participated, among whom 6652 (82.4 %) self-reported their ethnocultural background as White, 225 (2.8 %) as South Asian, 222 (2.8 %) East Asian, 214 (2.7 %) Southeast Asian, 197 (2.4 %) Black, and 28 (0.3 %) as Aboriginal. Based on their sociodemographic characteristics, participants from these ethnocultural minority groups were matched to White participants. Black participants reported significantly greater stressful life events than White participants (p = .04), particularly death (p < .05), divorce (p = .002) and financial difficulties (p < .001). East Asian participants reported significantly less social support than their White counterparts (p < .001), and this was not confounded by measurement variance. Mental health service use was significantly lower in all ethnocultural minorities except Aboriginals, when compared to White participants (p = .001). CONCLUSIONS: There is a high burden of psychosocial distress in several preponderant ethnocultural minorities in Ontario; many of whom are not accessing available mental health services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".