Predictors of the Longitudinal Course of Major Depression in a Canadian Population Sample
Bibliographic record
Abstract
OBJECTIVE: Most psychiatric epidemiologic studies have used cross-sectional methods, resulting in a lack of information about the longitudinal course of depressive disorders. The objective of our study was to describe the longitudinal epidemiology of major depressive episodes (MDEs) in a Canadian sample using data from the National Population Health Survey (NPHS). METHODS: The NPHS started data collection in 1994 and has evaluated past-year MDE using repeat interviews of the same cohort every 2 years since then. In our study, we examined the number of weeks depressed during years when MDEs occurred, the proportion of respondents having MDEs at consecutive cycles, and MDE counts during follow-up. RESULTS: A sizable proportion of MDEs were brief: about one-half of respondents with past-year MDE reported 8 or fewer weeks of depression during that year. Less than 10% reported that they were depressed for the entire year. However, a larger proportion (19.1%) fulfilled criteria for MDE on consecutive interview cycles, suggesting either persistence or rapid recurrence. The mean number of detected MDEs among those with at least 1 detected MDE up to 2006 was 2. Positive family history, evidence of comorbidity, negative cognitive style, stress, pain, and smoking were associated with a more negative course. CONCLUSIONS: The longitudinal course of MDE in the general population is heterogeneous, including a mixture of brief and more protracted MDEs. Many risk factors for MDE are also associated with a negative course, exceptions being (younger) age and sex. These epidemiologic observations may assist with identification of patients requiring more intensive management in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".