Why Do University Students From Australia, New Zealand, and Argentina Play Drinking Games? A Mixed-Method Cross-Country Study
Bibliographic record
Abstract
Qualitative work suggests that young people’s motives for playing drinking games (DGs) extend beyond those assessed in the Motives for Playing Drinking Games (MPDG) measure. Using a mixed-methods approach, we tested whether the 7-factor model of the MPDG would emerge among university students from Australia, New Zealand, and Argentina, and whether their open-ended responses regarding their reasons for playing would map onto the MPDG subscales. Students ( N = 895; ages = 18–30 yrs) completed the MPDG-33 measure and an open-ended-question regarding their reasons for playing DGs. We found support for the 7-factor model of the MPDG among students across sites. Open-ended responses revealed that students were motivated to play for a variety of reasons, some of which overlapped with the MPDG subscales while others did not. We present a conceptual model that considers motives specific to alcohol consumption in the context of a DG and reasons/possible motives for playing a DG given its specific features.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".