Variations in the Prevalence of Psychiatric Disorders and Social Problems across Canadian Provinces
Bibliographic record
Abstract
OBJECTIVE: To determine provincial 12-month prevalence rates for selected psychiatric disorders and to assess the association between these and the Canadian Social Problem Index (SPI). METHOD: Psychiatric data for depression, mania, panic disorder, social phobia, and agoraphobia were derived from the results of the 2002 Canadian Community Health Survey: Mental Health and Well-Being. The Canadian SPI was updated for 2002, and correlations were calculated between the SPI and the 5 diagnostic prevalence values across provinces. RESULTS: The results showed that the SPI had maintained its tendency to increase from east to west in Canada, a trend reflected by depression and mania. The psychiatric disorders did not show strong correlations with the SPI in 2002, but depression and mania did show relatively strong associations with index values from earlier years. High-to-low ratios across provinces for individual social problems averaged over 5, and the results were essentially of the same magnitude for the ranges of particular psychiatric diagnoses. CONCLUSIONS: The differences in need found here suggest that per capita allocation of funding for mental health and social programs may not be appropriate. The mixed findings on the association between mental disorders and social problem behaviour across provinces leads to more research questions than research answers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".