ALCOHOL CONSUMPTION AND MAJOR DEPRESSION IN THE GENERAL POPULATION: THE CRITICAL IMPORTANCE OF DEPENDENCE
Bibliographic record
Abstract
BACKGROUND: Substance use disorders and major depressive episodes (MDEs) often co-occur. Alcohol consumption may contribute to the aetiology of depressive episodes and/or vice versa. In Canada, the National Population Health Survey (NPHS) evaluated several aspects of alcohol use and MDE in a large population cohort over 12 years of follow-up. We evaluated the incidence of MDE in relation to different patterns of alcohol use, and examined the incidence of alcohol misuse in respondents with and without MDE. METHODS: The NPHS is a longitudinal study that began data collection in 1994 and whose cohort has been followed with biannual interviews. These interviews assess MDE using the Composite International Diagnostic Interview Short Form for Major Depression (CIDI-SFMD). Another CIDI-SF module assessed alcohol dependence during two interviews. Any alcohol consumption, exceeding moderate drinking guidelines and binge drinking were also assessed. We used logistic regression and proportional hazards models to assess longitudinal relationships between these variables RESULTS: Respondents with alcohol dependence were at higher risk of MDE, but any alcohol consumption, exceeding guidelines for moderate drinking and binge drinking were not. Respondents with MDE showed no increase of alcohol consumption, but the risk of alcohol dependence was elevated in depressed men. CONCLUSION: Of direct clinical significance is the bidirectional relationship between alcohol dependence and MDE. Associations between alcohol consumption and MDE were not observed except when dependence was indicated. Alcohol dependence increased the risk of MDE, and MDE increased the risk of alcohol dependence, but only in men.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".