Substance Use Among Collegiate Athletes Versus Non-athletes
Bibliographic record
Abstract
Purpose: To supplement the literature on substance misuse by collegiate athletes by expanding the number of substances typically examined and include mental health symptom covariates related to both sleep and substance use. Methods: Substance use was assessed with the following item: "Within the last 30 days, on how many days did you use?" with a list of 17 substances. Multinomial logistic regression analysis examined each substance variable as outcome and athlete status as predictor. Results: Findings of the fully adjusted model indicated that compared to non-athletes, collegiate athletes were most likely to be past users of alcohol, occasional users of smokeless tobacco, alcohol, and steroids, and frequent users of smokeless tobacco. Conclusions: The significant differences shown between collegiate athletes and non-athletes may reflect differences in intentions to improve performance, pain management, and stress management. Future studies should seek to elucidate the underreported and self-reported phenomena among this population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".