Bibliographic record
Abstract
BACKGROUND: Generally, public health strategies for major depression have focused on case-finding, public and professional education, and disease-management strategies. In principle, increased rates of treatment utilization and improved treatment outcomes should lead to improved mental health at the population level. Progress of this sort, however, has been difficult to confirm. METHODS: The National Population Health Survey (NPHS) is a large-scale longitudinal study of a representative sample drawn from the Canadian population. To date, Statistics Canada has released data from 3 NPHS cycles: 1994-1995, 1996-1997, and 1998-1999. Treatment utilization and major depression measures were employed in the NPHS survey, providing a unique source of longitudinal Canadian data. In this study, major depression point prevalence (defined using a predictive instrument for annual major depressive episode [MDE] prevalence and responses from a distress scale) and associated treatment utilization were evaluated over time. RESULTS: Between 1994-1995 and 1995-1996, the proportion of persons with depression receiving antidepressant treatment increased dramatically, from 18.2% (12.3% to 22.1%) in 1994-1995 to 32.6% (23.0% to 42.2%) in 1998-1999. Point prevalence of major depression was 2.4%, 1.8%, and 1.9% in the 3 NPHS iterations. CONCLUSIONS: Data from the NPHS suggest public health progress against major depression in Canada. More people with major depression in Canada are receiving treatment, and these changes may have been associated with improved population health status. However, both random variation and extraneous societal factors could account for the observed trends in prevalence. It is impossible to relate changes in utilization directly to population health status using the NPHS data.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".