Problem gambling and gaming in elite athletes
Bibliographic record
Abstract
BACKGROUND: High-level sports have been described as a risk situation for mental health problems and substance misuse. This, however, has been sparsely studied for problem gambling, and it is unknown whether problem gaming, corresponding to the tentative diagnosis of internet gaming disorder, may be overrepresented in athletes. This study aimed to study the prevalence and correlates of problem gambling and problem gaming in national team-level athletes. METHODS: A web-survey addressing national team-level athletes in university studies (survey participation 60%) was answered by 352 individuals (60% women, mean age 23.7), assessing mental health problems, including lifetime history of problem gambling (NODS-CLiP) and problem gaming (GASA). RESULTS: Lifetime prevalence of problem gambling was 7% (14% in males, 1% in females, p < 0.001), with no difference between team sports and other sports. Lifetime prevalence of problem gaming was 2% (4% in males and 1% in females, p = 0.06). Problem gambling and problem gaming were significantly associated (p = 0.01). CONCLUSIONS: Moderately elevated rates of problem gambling were demonstrated, however with large gender differences, and interestingly, with comparable prevalence in team sports and in other sports. Problem gaming did not seem more common than in the general population, but an association between problem gambling and problem gaming was demonstrated.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".