Examining drinking game harms as a function of gender and college student status.
Bibliographic record
Abstract
Drinking game (DG) participation among young adults is widespread. Because heavy alcohol consumption is commonly associated with playing DGs, this activity presents a health risk for those who play. In the present study, we explored the most common negative DG consequences experienced by young adults and how DG consequences differed by gender and college status. Participants were young adult drinking gamers (N = 1,600; age 18-25; Mage = 22.6; 47% men; 41% noncollege students; 77% White) recruited from Amazon's Mechanical Turk. They completed an online anonymous survey which included items on the Brief Young Adult Alcohol Consequences Questionnaire that were modified to measure DG consequences experienced in the past month. Over half of the participants reported experiencing a hangover, saying/doing embarrassing things, having less energy, and feeling sick as a result of playing DGs. Using IRT analysis, we also found differential item functioning (DIF) on several items across gender and college status. We then created a short-form version of the DG consequences measure that excluded items demonstrating DIF, and based on this modified measure, we examined differences in severity of negative DG consequences as a function of gender and college status. Controlling for age, college status, DG frequency/consumption, and alcohol use on non-DG occasions, we found that men experienced slightly more DG consequences than women. Similar findings emerged for college students compared to noncollege students. This study is an important first step toward understanding who is most at risk for experiencing certain types of negative DG consequences and how researchers/practitioners could measure this construct. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".