Problem Gambling and Problem Gaming in Elite Athletes: a Literature Review
Bibliographic record
Abstract
Researchers have raised concerns about mental health in elite athletes, including problem gambling, where research hitherto is scarce. While gambling has been assessed in the younger student-athlete population, neither gambling nor the recently recognized behavioral addiction of gaming disorder has been sufficiently addressed in the elite athlete population. The present systematic literature review aimed to summarize research knowledge on the prevalence and correlates of problem gambling and problem gaming in elite athletes. Research papers were searched systematically using the Scopus, PsycINFO, and PubMed/MEDLINE databases and evaluated following a PRISMA paradigm. For the elite athlete population, eight reports on problem gambling and one report on problem gaming were found. While at least five papers indicated an increased risk of problem gambling in elite athletes compared to the general population, one study from Australia indicated the opposite. Problem gambling was generally more common in male athletes. Knowledge of problem gaming prevalence is thus far limited. It is concluded that increased research in problem gambling and problem gaming in elite athletes is warranted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".