Sociodemographic Factors Associated with Comorbid Major Depressive Episodes and Alcohol Dependence in the General Population
Bibliographic record
Abstract
OBJECTIVES: To estimate the 12-month prevalence of alcohol dependence (AD) among subjects with major depressive episodes (MDEs) and the 12-month prevalence of MDEs among those with AD; to investigate the associations between demographic and socioeconomic characteristics and comorbid MDE and AD, based on established theoretical models; and to compare the rates of mental health service use between groups having high and low risk for comorbid conditions. METHODS: We used data from the 1996-1997 Canadian National Population Health Survey. MDE and AD were measured using the World Health Organization's Composite International Diagnostic Interview Short Form (CIDI-SF). We calculated the 12-month prevalence of MDEs among participants with AD and of AD among those with MDEs. The associations between demographic and socioeconomic characteristics and comorbidity were investigated. RESULTS: Of participants with MDEs, 8.6% had AD; 19.6% of participants with AD reported having at least 1 MDE in the past 12 months. Being young (aged 12 to 24 years); being divorced, separated, or widowed; and having low family income level were positively associated with MDE, AD, and comorbidity. Among participants with comorbid MDE and AD, those who were aged 12 to 24 years were less likely to have used any mental health services in the past 12 months than were others. CONCLUSIONS: Young age, single marital status, and low family income may be potential risk factors for comorbid MDE and AD. Although AD is rare in the general population, public health interventions that target the groups identified as at risk may help to prevent MDE, AD, and comorbidity.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".