Problem gambling and associated mental health concerns in elite athletes: a narrative review
Bibliographic record
Abstract
Opportunities to participate in gambling have dramatically changed during the past 20 years. Casinos have proliferated as have electronic gambling machines, lotteries, sports betting, and most recently online gambling. Gambling among the general population has moved from being perceived negatively to a socially acceptable pastime. As over 80% of individuals have reported gambling for money during their lifetime, governments recognise that regulating gambling-a multibillion dollar industry-is a significant source of revenue. While the vast majority of individuals engaged in some form of gambling have no or few gambling-related problems, an identifiable proportion of both adolescents and adults experience significant gambling-related problems. Elite athletes have not been immune to the lure of gambling nor its concomitant problems. Prevalence studies suggest higher rates of gambling problems among athletes than the general population. In this narrative review, we examine several risk factors associated with gambling problems among elite athletes and new forms of gambling that may be problematic for this population. Given the potential serious mental health and performance consequences associated with a gambling disorder for athletes, we aim to increase coaches', athletic directors' and health professionals' knowledge concerning the importance of screening and treatment referrals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".