Validation of a Seven-Factor Structure for the Motives for Playing Drinking Games Measure
Why this work is in the frame
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Bibliographic record
Abstract
Playing drinking games can be characterized as a high-risk drinking activity because games are typically designed to promote heavy alcohol consumption. While research suggests that young adults are motivated to play drinking games for a variety of reasons (e.g., for thrills/fun, for the competition), the Motives for Playing Drinking Games measure has received limited empirical attention. We examined the psychometric properties of this measure with a confirmation sample of young adults recruited from Amazon's MTurk ( N = 1,809, ages 18-25 years, 47% men; 41% not currently enrolled in college) and a validation sample of college students ( N = 671; ages 18-23 years; 26% men). Contrary to the 8-factor model obtained by Johnson and Sheets in a study published in 2004, examination of the factor structure with our confirmation sample yielded a revised 7-factor model that was invariant across race/ethnicity and college student status. This model was also validated with the college student sample. In the confirmation sample, enhancement/thrills and sexual pursuit motives for playing drinking games were positively associated with gaming frequency/consumption and negative gaming consequences. Furthermore, conformity motives for playing drinking games were positively associated with negative gaming consequences, while competition motives were positively associated with gaming frequency. These findings have significant implications for research and prevention/intervention efforts.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it