The Epidemiology of Mental and Substance Use—Related Disorders among White, Chinese, and other Asian Populations in Canada
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to estimate and compare the lifetime and 12-month prevalence of mood disorders, anxiety disorders, and substance dependence in white, Chinese, and other Asian populations in Canada and to identify correlates of mental disorders in these ethnic groups. METHODS: We used data from the Canadian Community Health Survey: Mental Health and Well-Being. The WHO's Composite International Diagnostic Interview was used to assess mental disorders diagnosed according to the DSM-IV criteria. We included subjects who were white (n = 33399), Chinese (n = 733), or from other Asian populations (n = 1113). The lifetime and 12-month prevalence of mental disorders was estimated according to ethnic group. RESULTS: The lifetime and 12-month prevalence of mental disorders in Chinese participants was lower than the prevalence rates in white participants. Other Asian participants were less likely than white individuals to have had any mood and anxiety disorder in their lifetime. The 12-month prevalence of any mental disorder in Chinese participants was lower than in other Asian participants. However, the proportion of Chinese participants with perceived fair to poor mental health was higher than in the other Asian and white groups. CONCLUSIONS: The prevalence of mental disorders among Chinese individuals living in Canada resembles that in China's population. The prevalence of mental disorders in the Asian populations may vary by region. Studies are needed to examine subthreshold mental disorders in the Asian populations as well as ethnic differences in mental disorders in relation to sex, age, and clinical condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".