Drinking Game Behaviors among College Students: How Often and How Much?
Bibliographic record
Abstract
BACKGROUND: Participation in drinking games (DG) has been identified as a common health-risk behavior among college students. However, research suggests that the frequency of DG participation alone may not pose a significant health risk; rather, gaming may be most hazardous when large amounts of alcohol are consumed. OBJECTIVES: The present study was designed to examine whether specific gaming behaviors (frequency of play and amount of consumption) place gamers at elevated risk for negative drinking outcomes. METHOD: Students from 30 U.S. colleges completed self-report questionnaires via the Internet about their drinking attitudes and behaviors. Four groups of student gamers (N = 2,230) were examined: low frequency/low consumption (n = 1,047), low frequency/high consumption (n = 453), high frequency/low consumption (n = 326), and high frequency/high consumption (n = 404). RESULTS: Multilevel regression analyses indicated that the frequency x consumption interaction emerged as a significant predictor of negative drinking consequences. Follow-up analyses indicated that quantity of alcohol consumed during DG predicted negative drinking consequences for high-frequency gamers only. CONCLUSIONS/SCIENTIFIC CONTRIBUTION: The present results challenge the assumption that all drinking-gaming practices pose equivalent health risks for gamers. Considering only participation in or level of consumption during DG may not tell the complete story with respect to the health hazards involved with gaming behaviors among college students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".