Micro-level factors associated with alcohol use and binge drinking among youth in the COMPASS study (2012/13 to 2017/18)
Bibliographic record
Abstract
INTRODUCTION: This study examined the associations of micro-level factors with current alcohol use and binge drinking among a large sample of Canadian youth. METHODS: This descriptive-analytical study was conducted among high school students enrolled in the COMPASS study between 2012/13 and 2017/18. We used generalized estimating equations modelling to determine associations between micro-level factors and likelihood of current versus non-current alcohol use and binge drinking among respondents. RESULTS: Students reporting current cannabis use were more likely to report current alcohol use over never use (odds ratio [OR] = 4.46, 95% confidence interval [CI]: 4.33-4.60) compared to students reporting non-current cannabis use. Students reporting current smoking of tobacco products were more likely to report current binge drinking over never binge drinking (OR = 2.52, 95% CI: 2.45-2.58), compared to non-smoking students. Students reporting weekly disposable incomes of more than $100 were more likely to report current over never binge drinking (OR = 2.14, 95% CI: 2.09-2.19), compared to students reporting no weekly disposable income. CONCLUSION: Higher disposable incomes, smoking of tobacco products and use of cannabis were associated with current alcohol use and binge drinking among youth. Findings may inform design of polysubstance use prevention efforts in high schools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".