Problem gambling among elite ice hockey players in Sweden – elevated prevalence among male, but not female athletes
Bibliographic record
Abstract
OBJECTIVES: An emerging body of research reveals a heightened risk of gambling problems among elite athletes, particularly among males, but these studies often suffer from small sample sizes and lack diverse representation across sports and groups. This study aimed to investigate gambling problems and their correlates among elite male and female ice hockey players in Sweden's top leagues. METHODS: During the labor union's on-site visits to Swedish ice hockey clubs in the top two tiers for males and the top tier for females, a web-based survey was conducted. Players were screened for gambling problems using the Problem Gambling Severity Index, and for depression, anxiety, and hazardous drinking using other standardized instruments. Estimated study participation was 75-80%. RESULTS: Among male athletes, 12% met the criteria for moderate-risk or problem gambling, while none of the females met this threshold. Approximately 24% of male and 2% of female participants reported any degree of at-risk gambling. In males, gambling problems were strongly associated with depressive and anxiety symptoms and with hazardous alcohol consumption. CONCLUSION: Gambling problems are 3-4 times more prevalent among elite male ice hockey players compared to young men in the general population. The authors discuss the associated mental health consequences, vulnerability to match-fixing-related fraud, and the need for preventive measures and easy access to treatment.
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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 | 0.001 |
| 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".