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%.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".