Gambling by college athletes: An association between problem gambling and athletes
Bibliographic record
Abstract
This investigation compares the prevalence rates of pathological and problem gambling between college athletes and non-athletes. Participants in the study included 954 students enrolled in health and safety classes from nine universities belonging to the Southeastern Conference (SEC). Of these students, 129 (14%) were classified as athletes. The South Oaks Gambling Screen (SOGS), designed to measure pathological gambling, was used as the testing instrument. Participants were asked additional questions to determine athletic participation and to gather demographic information. Cross tabulations, Pearson chi-square tests and Cramer's V tests were used to determine if there were significant associations between groups. On the whole, significant associations were not found between athletes and non-athletes and pathological and problem gambling; however, a statistically significant association was found between problem gambling and female athletes. The prevalence rates of pathological and problem gambling among athletes were 6.2% and 6.2%, while the prevalence rates among non-athletes were 3.4% and 3.3%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".