Correlates of Depressive and Anxiety Disorders among Young Canadians
Bibliographic record
Abstract
OBJECTIVE: The current study presents data on the prevalence of depressive and anxiety disorders in the Canadian population aged between 15 and 24 years and examines their potential correlates. METHODS: The study is based on the 2002 Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2). This survey was administered to a representative sample of 36,984 Canadians. A subsample of 5673 Canadians aged between 15 and 24 years was available for the analyses. We used descriptive analyses to calculate lifetime and 12-month prevalence of depressive and anxiety disorders, and we used logistic regressions to measure odds ratios. RESULTS: Among Canadian youths, 10.2% had suffered from depressive disorders during their lifetime, whereas 12.1% had suffered from anxiety disorders. For 12-month prevalence, the rates were 6.4% and 6.5% for depressive and anxiety disorders, respectively. Depressive disorders were more frequent among youth aged 20 to 24 years and among those no longer in school. Both disorders were more common among women and people under extreme stress. CONCLUSIONS: The prevalence rates found are comparable with other studies, and most of the correlates are concordant with the literature. Results indicate that there is a turning point for depression between late adolescence and adulthood that could be crucial for intervention planning.
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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 | 0.002 | 0.003 |
| 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".