Descriptive Epidemiology of Major Depression in Canada
Bibliographic record
Abstract
OBJECTIVE: The Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2) is the first national study to use a full version of the Composite International Diagnostic Interview. For this reason, and because of its large sample size, the CCHS 1.2 is capable of providing the best currently available description of major depression epidemiology in Canada. Using the CCHS 1.2 data, our study aimed to describe the epidemiology of major depression in Canada. METHOD: All estimates used appropriate sampling weights and bootstrap variance estimation procedures. The analysis consisted of estimating proportions supplemented by logistic regression modelling. RESULTS: The lifetime prevalence of major depressive episode was 12.2%. Past-year episodes were reported by 4.8% of the sample; 1.8% reported an episode in the past 30 days. As expected, major depression was more common in women than in men, but the difference became smaller with advancing age. The peak annual prevalence occurred in the group aged 15 to 25 years. The prevalence of major depression was not related to level of education but was related to having a chronic medical condition, to unemployment, and to income. Married people had the lowest prevalence, but the effect of marital status changed with age. Logistic regression analysis suggested that the annual prevalence may increase with age in men who never married. CONCLUSIONS: The prevalence of major depression in the CCHS 1.2 was slightly lower than that reported in the US and comparable to pan-European estimates. The pattern of association with demographic and clinical variables, however, is broadly similar. An increasing prevalence with age in single (never-married) men was an unexpected finding.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| 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".