Alcohol Use Disorder and the Persistence/Recurrence of Major Depression: Le trouble de l’usage de l’alcool et la persistance/récurrence de la dépression majeure
Bibliographic record
Abstract
Objective This study aims to determine the role of alcohol use disorder and other potential risk factors on persistence/recurrence of major depression in a Canadian population sample. Methods Data were drawn from the National Population Health Survey (1994/1995 to 2010/2011), a prospective epidemiologic survey of individuals 12 years and older, living in 10 Canadian provinces ( N = 17,276). Participants were reinterviewed every 2 years for 9 cycles. This study population was a cohort of individuals who at baseline met the diagnosis of a major depressive episode (MDE) in the previous 12 months ( n = 908). After the 6-year (cycle 4) and 16-year (cycle 9) follow-up period, 124 of 718 participants and 79 of 461 participants met the criteria for MDE, respectively. Persistence or recurrence of major depression was defined as meeting a diagnosis of MDE after 6 years and 16 years. Modified Poisson regression models were used to assess the role of alcohol dependence and other risk factors on the persistence/recurrence of major depression using Stata 14. Results Alcohol use disorder was significantly correlated with a 6-year (odds ratio [ OR]: 3.03; 95% confidence interval [CI], 1.68 to 5.48; P < .0001) and 16-year ( OR, 3.17; 95% CI, 1.15 to 8.77, P = 0.003) persistence/recurrence of major depression. Other factors associated with the persistence/recurrence of major depression include female sex, childhood traumatic events, chronic pain restricting activities, daily smoking, and low self-esteem. Conclusions Comorbid alcohol use disorder was found to be a strong risk factor for the persistence or recurrence of major depression.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".