The Prevalence of Major Depression is Not Changing
Bibliographic record
Abstract
OBJECTIVE: To evaluate trends in the prevalence of major depressive episodes (MDEs) in Canada during the past 2 decades using data collected in a series of national surveys. METHOD: MDE prevalence has been assessed in national surveys that either used a short form version of the Composite International Diagnostic Interview Short Form for Major Depression (CIDI-SFMD) or an adaptation of the World Health Organization's (full-length) version, World Mental Health (WMH) CIDI. We applied meta-regression methods to adjust for instrument type while also addressing design effects in the individual data sets. Interprovincial differences that might have confounded estimation of national trends were also explored. RESULTS: Interprovincial differences were not found to be significant, nor were time by province interactions. Estimates based on the WMH-CIDI were about 1% lower than those using the CIDI-SFMD. There was no evidence of changing prevalence over time, with slope for time, adjusted for assessment instrument, being nearly zero (β=0.0007, P=0.24). CONCLUSION: An extensive collection of surveys conducted in Canada between 1994 and 2012 provide an opportunity to examine long-term trends in the prevalence of major depression. MDE prevalence has not changed during this period of time.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".