The Epidemiology of Major Depression: Implications of Occurrence, Recurrence, and Stress in a Canadian Community Sample
Bibliographic record
Abstract
OBJECTIVE: To study the effects of stress process variables on the prevalence of major depressive disorder (MDD) and to explore the factors related to its onset and recurrence, using measures of stress and disorder that are more comprehensive than those in previous studies of depression. METHOD: Data were collected from a randomly selected community sample of 1393 adult respondents in Toronto, Ontario. Depression was measured using the Composite International Diagnostic Interview (CIDI). RESULTS: Bivariate and multivariate analyses examine demographic and stress process correlates of MDD to assess their impact on both occurrence and recurrence. The effects of childhood experiences on lifetime MDD are examined as risk factors for the initial onset of depression. With respect to occurrence, the multivariate analyses tended to show agreement with established findings. Results using a subsample of people with lifetime depression to predict recurrence tended to mirror results using the full sample, except with respect to gender, birthplace, and, to a certain degree, stress differences. Finally, traumas experienced in childhood, as well as early childhood experiences involving parental substance abuse and mental health problems, were significant predictors of the onset of major depressive disorder. CONCLUSION: This research currently represents the only Canadian community study to examine the relationship between stress, social support, coping, and depression using outcome measures that approximate Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. In addition, it is among the few that use a comprehensive measure of life stress encompassing childhood experiences and current operant stress (both events and chronic problems).
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 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) | 0.001 | 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".