A Longitudinal Population-Based Study of Treated and Untreated Major Depression
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated the factors associated with different outcomes in individuals with major depressive episode (MDE) in relation to mental health service utilization. OBJECTIVES: This study was to, in depressed individuals who used and did not use mental health services, 1) compare the demographic, psychosocial, and clinical characteristics; 2) estimate the risk of MDE in a 6-year follow-up period; and 3) identify the factors associated with the persistence/recurrence of MDE. DESIGN: This was a population- based longitudinal analysis. SUBJECTS: Participants included the longitudinal cohort of the Canadian National Population Health Survey who reported MDE at the baseline survey (n = 609). MEASURES: MDE was measured by the Composite International Diagnostic Interview-Short Form for Major Depression. RESULTS: In the 6-year follow-up period, 49.8% of participants with treated depression developed subsequent MDE; 28.7% of those with untreated depression reported MDE. Multivariate analyses showed that, among those who reported the use of mental health services, childhood and adulthood traumatic events and functional impairment were related to the recurrence of MDE. Among those who did not use mental health services, reported negative life events and the severity of depressive symptoms were predictive of recurrent MDE. CONCLUSIONS: The risk of the recurrence of MDE and associated factors differ in mental health service users and nonusers. Future studies need to confirm these results and to identify service barriers for those who do not use the services and who are at a high risk of MDE.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".