Test-Retest Reliability of the 7-Factor Motives for Playing Drinking Games Scale and its Associations With Drinking Game Behaviors Among Female College Athletes
Bibliographic record
Abstract
Drinking games (DGs) participation is prevalent among college-attending emerging adults. Research also suggests that student-athletes play DGs more frequently than non student-athletes, but what motivates student-athletes to participate in DGs is not well understood. Using data from a larger longitudinal study with Division III female athletes, we examined the test-retest reliability and minimal detectable change of the revised 7-factor Motives for Playing Drinking Games (MPDG) measure, and explored how its subscales were related to DGs behavior across two annual timepoints ( n = 49). Results indicated that the MPDG shows adequate test-retest reliability over a one year period among student-athletes. Controlling for age and general alcohol consumption, conformity motives were positively associated with DG consumption at timepoint 1, whereas the DG motives of enhancement/thrills and boredom were positively related to DG consumption at timepoint 2. Implications for future research directions on motives for playing DGs and DGs behavior among student-athletes are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".