Estimated Prevalence of the Seasonal Subtype of Major Depression in a Canadian Community Sample
Bibliographic record
Abstract
OBJECTIVE: To examine estimates of lifetime prevalence of seasonal affective disorder (SAD) in Toronto, Ontario. METHOD: Random telephone numbers were generated for the city of Toronto, and 781 respondents completed a telephone interview. Trained nonphysician interviewers conducted all interviews, which involved structured questions for diagnosing major depression. Patterns of symptom change across seasons were evaluated to establish a diagnosis of SAD according to DSM-III-R criteria. RESULTS: Correcting for sex and age, the prevalence of SAD defined by DSM-III-R criteria was 2.9% (95% CI, 1.7% to 4.0%), and the overall lifetime prevalence of major depression in the sample was 26.4% (95% CI, 23.3% to 29.4%). Some subjects were contacted for a follow-up interview conducted in person; the positive predictive value for the diagnosis of major depression for the telephone interview was 100%, and the negative predictive value was 93%. CONCLUSIONS: The seasonal subtype of depression represents 11% of all subjects with major depression, suggesting that SAD is a significant public health concern. The telephone interview demonstrated adequate reliability, indicating that it is appropriate for epidemiological surveys of this nature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".