Age at first alcohol use predicts current alcohol use, binge drinking and mixing of alcohol with energy drinks among Ontario Grade 12 students in the COMPASS study
Bibliographic record
Abstract
INTRODUCTION: This study investigates the influence of age at first use of alcohol on current alcohol use and associated behaviours in a large sample of Canadian youth. METHODS: This descriptive-analytical study was conducted among Ontario Grade 12 students enrolled in the COMPASS Host Study between 2012 and 2017. We used generalized estimating equations (GEE) modelling to determine associations between age at first alcohol use and likelihood of current versus non-current alcohol use, binge drinking and mixing of alcohol with energy drinks among respondents. RESULTS: Students reporting an age at first alcohol use between ages 13 and 14 years were more likely to report current alcohol use versus non-current use (OR = 2.80, 95% CI: 2.26-3.45) and current binge drinking versus non-current binge drinking (OR = 3.22, 95% CI: 2.45-4.25) compared to students reporting first alcohol use at age 18 years or older. Students who started drinking at 8 years of age or younger were more likely to report current versus non-current alcohol use (OR = 3.54, 95% CI: 2.83-4.43), binge drinking (OR = 3.99, 95% CI: 2.97-5.37), and mixing of alcohol with energy drinks (OR = 2.26, 95% CI: 1.23-4.14), compared to students who started drinking at 18 years or older. CONCLUSION: Starting to drink alcohol in the early teen years predicted current alcohol use, current binge drinking and mixing of alcohol with energy drinks when students were in Grade 12. Findings indicate a need for development of novel alcohol prevention efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".