Problem Gambling in the Fitness World—A General Population Web Survey
Bibliographic record
Abstract
The world of sports has a complex association to problem gambling, and the sparse research examining problem gambling in athletes has suggested an increased prevalence and particularly high male predominance. The present study aimed to study frequency and correlates of problem gambling in populations with moderate to high involvement in fitness or physical exercise. This is a self-selective online survey focusing on addictive behaviors in physical exercise distributed by 'fitness influencers' on social media and other online fitness forums to their followers. Respondents were included if they reported exercise at least thrice weekly, were above 15 years of age, and provided informed consent (N = 3088). Problem gambling, measured with the Lie/Bet, was studied in association with demographic data, substance use, and mental health variables. The occurrence of lifetime problem gambling was 8 percent (12 percent in men, one percent in women). In logistic regression, problem gambling was associated with male gender, younger age, risky alcohol drinking, obsessive-compulsive disorder, and less frequent exercise habits. In conclusion, in this self-recruited population with moderate to high fitness involvement, problem gambling was moderately elevated. As shown previously in elite athletes, the male predominance was larger than in the general population. The findings strengthen the link between problem gambling and the world of sports.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".