CORRELATES OF BINGE DRINKING IN A SAMPLE OF CANADIAN UNIVERSITY STUDENTS
Bibliographic record
Abstract
There is a lack of recent research exploring the differences between binge and non-binge drinkers among Canadian university students. The current study aims to address this gap in the literature through an exploratory statistical analysis. Univariate, bivariate, and multivariate (logistic regression) analyses were employed to identify the prevalence of binge drinking, as well as its various sociodemographic, behavioural, and mental health correlates. A large majority (83.1%) of the 507 respondents reported consuming alcohol in the previous 12 months; of these, 69.7% (67.0% of males, 70.9% of females) reported engaging in binge drinking (5 drinks in one session for males, 4 for females) at least once in the previous 30 days. Although there was no significant gender difference in the prevalence of binge drinking, there were several gender differences among the correlates. Compared to non-binge drinkers, male binge drinkers reported greater life satisfaction, and greater probability of smoking cigarettes and engaging in risky sex, while females reported greater impulsivity and lower religiosity. As expected, binge drinkers experienced more adverse consequences from alcohol consumption than did non-binge drinkers, but unexpectedly did not differ significantly in mental health and wellbeing. Limitations of the present study and future research directions are discussed with a view to improving our understanding of risk and protective factors related to unhealthy alcohol consumption among university students in Canada and abroad.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 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) | 0.002 | 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".