Profiles and Mental Health Correlates of Alcohol and Illicit Drug Use in the Canadian Population: An Exploration of the J-Curve Hypothesis
Bibliographic record
Abstract
OBJECTIVE: Alcohol and (or) illicit drug use (AIDU) problems are associated with mental health difficulties, but low-to-moderate alcohol consumption may have mental health benefits, compared with abstinence. Our study aimed to explore the hypothesis of a nonlinear, or J-curve, relation between AIDU profiles and psychological distress, psychiatric disorders, and mental health service use in the general Canadian population. METHODS: Data were collected from a representative sample of the Canadian population (n = 36 984). Multiple correspondence analyses and cluster analyses were used to extract AIDU profiles. Sociodemographics, psychological distress, psychiatric disorders, and mental health service use were assessed and compared between profiles. RESULTS: Seven AIDU profiles emerged, including 3 involving risky or problematic AIDU that correlate with major affective disorders, anxiety disorders, suicidal behaviours, and higher levels of psychological distress. No J-curve relation was found for psychiatric disorders and mental health service use. The lifetime-abstainer profile correlates with the lowest rates of psychiatric disorders and mental health service use. Lifetime abstainers are also more often female, immigrant, and unemployed. Compared with other profiles, spirituality is more important in their life. CONCLUSIONS: The hypothesis of a nonlinear relation between psychiatric disorders and AIDU was not supported. Lifetime AIDU abstainers have specific sociodemographic and cultural background characteristics in Canada.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".