Prevalence of Mental Disorders and Suicidality in Canadian Provinces
Bibliographic record
Abstract
OBJECTIVE: There is limited information to guide health-care service providers and policy makers on the burden of mental disorders and addictions across the Canadian provinces. This study compares interprovincial prevalence of major depressive disorder (MDD), bipolar disorder, generalized anxiety disorder (GAD), alcohol use disorder, substance use disorders, and suicidality. METHOD: = 25,113), a representative sample of Canadians over the age of 15 years across all provinces. Cross tabulations and logistic regression were used to determine the prevalence and odds of the above disorders for each province. Adjustments for provincial sociodemographic factors were performed. RESULTS: The past-year prevalence of all measured mental disorders and suicidality, excluding GAD, demonstrated significant interprovincial differences. Manitoba exhibited the highest prevalence of any mental disorder (13.6%), reflecting high prevalence of MDD and alcohol use disorder compared to the other provinces (7.0% and 3.8%, respectively). Nova Scotia exhibited the highest prevalence of substance use disorders (2.9%). Quebec and Prince Edward Island exhibited the lowest prevalence of any mental disorder (8.5% and 7.7%, respectively). Manitoba also exhibited the highest prevalence of suicidal ideation (5.1%); however, British Columbia and Ontario exhibited the highest prevalence of suicidal planning (1.4% and 1.3%, respectively), and Ontario alone exhibited the highest prevalence of suicide attempts (0.7%). CONCLUSIONS: Significant interprovincial differences were found in the past-year prevalence of mental disorders and suicidality in Canada. More research is necessary to explore these differences and how they impact the need for mental health services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".