Racial, ethnic, and sex differences in heavy drinking and negative alcohol-related consequences in a national sample of NCAA student-athlete drinkers
Bibliographic record
Abstract
Objective Athletic involvement is linked to increased risk for heavy alcohol use among college students. We examined whether student-athletes from diverse racial/ethnic backgrounds differ with respect to heavy drinking and related consequences. Method: Participants were 15,135 student-athlete drinkers (50.7% female) from 170 NCAA member institutions who participated in an online study. Results: Findings from our hierarchical linear models indicated that being a male student-athlete was associated with an increased likelihood of high intensity drinking (10/8 + drinks/per sitting for males/females) for White, Asian American/Pacific Islander, and Black student-athletes, but not for Hispanic student-athletes. Additionally, being a female student-athlete was associated with higher levels of negative alcohol-related consequences across all racial/ethnic groups. Finally, at similar drink quantities, compared to being a White student-athlete, being an Asian American/Pacific Islander student-athlete was associated with higher levels of alcohol-related consequences. Conclusions: Student-athlete drinkers are not homogeneous with respect to heavy drinking and related consequences.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".