Participation in Drinking Games and Predrinking Among University Students in Argentina, Australia, Canada, and New Zealand
Bibliographic record
Abstract
AIMS: The limited existing research on drinking games and predrinking among university students in Argentina, Australia, Canada, and New Zealand suggests that participation in these risky drinking practices is relatively widespread among this population. Drinking norms and alcohol use can vary across countries and in different regions of the globe. The measurement of drinking games and predrinking participation between studies also differs, making cross-country comparisons difficult. The present study explored differences in past month participation in drinking games and predrinking among university students from a large public university in Argentina, Australia, Canada, and New Zealand. METHODS: The data analytic sample consisted of 1134 university students (ages 18-25, Mage = 20.2 years; 72.6% women) from Argentina (n = 349), Australia (n = 280), Canada (n = 262), and New Zealand (n = 243) who reported weekly alcohol consumption. Students completed a confidential survey on drinking attitudes and behaviors. RESULTS: Controlling for age, gender, and weekly drink consumption, there were no cross-country differences in past month participation in predrinking. In contrast, university students from Canada and New Zealand were more likely to have played a drinking game in the past month than students from Australia and Argentina. CONCLUSIONS: The present finding suggest that university students from Argentina, Australia, Canada, and New Zealand are equally likely to participate in predrinking regardless of country; however, the likelihood of playing drinking games differs as a function of country site.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".