Ethnic Differences in Mental Health Status and Service Utilization: A Population-Based Study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to compare the prevalence of self-reported mental health factors, mental health service use, and unmet needs across the 4 largest ethnic groups in Ontario, Canada: white, South Asian, Chinese, and black groups. METHODS: The study population was derived from the Canadian Community Health Survey, using a cross-sectional sample of 254,951 white, South Asian, Chinese, and black residents living in Ontario, Canada, between 2001 and 2014. Age- and sex-standardized prevalence estimates for mental health factors, mental health service use, and unmet needs were calculated for each of the 4 ethnic groups overall and by sociodemographic characteristics. RESULTS: We found that self-reported physician-diagnosed mood and anxiety disorders and mental health service use were generally lower among South Asian, Chinese, and black respondents compared to white respondents. Chinese individuals reported the weakest sense of belonging to their local community and the poorest self-rated mental health and were nearly as likely to report suicidal thoughts in the past year as white respondents. Among those self-reporting fair or poor mental health, less than half sought help from a mental health professional, ranging from only 19.8% in the Chinese group to 50.8% in the white group. CONCLUSIONS: The prevalence of mental health factors and mental health service use varied widely across ethnic groups. Efforts are needed to better understand and address cultural and system-level barriers surrounding high unmet needs and to identify ethnically tailored and culturally appropriate clinical supports and practices to ensure equitable and timely mental health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".