Ethnic density of regions and psychiatric disorders among ethnic minority individuals
Bibliographic record
Abstract
BACKGROUND: Ethnic minorities form an increasingly large proportion of Canada's population. Living in areas of greater ethnic density may help protect mental health among ethnic minorities through psychosocial pathways such as accessibility to culturally appropriate provision of mental health care, less discrimination and a greater sense of belonging. Mood and anxiety disorders are common psychiatric disorders. AIM: This study examined whether ethnic density of regions was related to mood and anxiety disorders among ethnic minorities in Canada. METHOD: Responses by ethnic minority individuals to the 2011-2014 administrations of the Canadian Community Health Survey ( n = 33,201) were linked to health region ethnic density data. Multilevel logistic regression was employed to model the odds of having mood and/or anxiety disorders associated with increasing region-level ethnic density and to examine whether sense of community belonging helped explain variance in such associations. Analyses were adjusted for individual-level demographic factors as well as region-level socio-economic factors. RESULTS: Higher ethnic density related to lower odds of mood and/or anxiety disorders for Canadian-born (but not foreign-born) ethnic minorities. Sense of community belonging did not help explain such associations, but independently related to lower odds of mood and/or anxiety disorders. These findings remained after adjusting for regional population density and after excluding (rural/remote) regions of very low ethnic density. CONCLUSION: Ethnic density of regions in Canada may be an important protective factor against mental illness among Canadian-born ethnic minorities. It is important to better understand how, and for which specific ethno-cultural groups, ethnic density may influence mental health.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".