Major Depression in Canada: What Has Changed over the Past 10 Years?
Bibliographic record
Abstract
OBJECTIVE: Major depressive episodes (MDE) make an important contribution to disease burden in Canada. The epidemiology of MDE in the national population has been examined in 2 mental health surveys, one conducted in 2002 and the other in 2012. Our objective was to compare selected variables from the 2 surveys to determine whether changes have occurred in the prevalence, treatment, and impact of MDE. METHOD: The World Health Organization World Mental Health Composite International Diagnostic Interview was used in both surveys and the MDE module (which was not modified) was scored using the same algorithm. Some variables assessing impact and management of MDE were also identical in the 2 surveys. The analysis was based on frequency estimates and associated 95% confidence intervals. RESULTS: The annual prevalence of MDE was 4.7% (95% CI 4.3% to 5.1%) in 2012, nearly identical to 4.8% (95% CI 4.5% to 5.1%) in 2002. Receipt of potentially adequate treatment (defined as taking an antidepressant or 6 or more visits to a health professional for mental health reasons) increased from 41.3% in 2002 to 52.2% in 2012, mostly due to an increase in respondents reporting 6 or more visits. Use of second generation antipsychotics also increased. There was no evidence of diminishing prevalence or impact (as assessed by symptoms of distress). CONCLUSIONS: There appears to have been an increase in receipt of treatment for people with MDE and a changing pattern of management. However, it was not possible to confirm that the impact of MDE is diminishing as a result.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".