Sports Gambling and Drinking Behaviors Over Time
Bibliographic record
Abstract
Importance: Sports gambling has become one of the most accessible forms of gambling in the United States, and recent research suggests that sports gambling coupled with frequent alcohol use may have deleterious health consequences. Objective: To examine the trajectories of sports gambling frequency and alcohol-related problems over time and the associations between these trajectories. Design, Setting, and Participants: This survey study was a 2-year longitudinal study conducted in the United States. Participants were recruited from a nonprobability internet panel from 2 sources: a large cross-section of adults matched and weighted to US Census norms and a specific oversample of sports-gambling adults. Recruitment began in spring 2022, and the last surveys concluded in spring 2024. To identify trajectories within sports gambling frequency and alcohol use problems, latent growth curve modeling was used. Main Outcomes: At each time point, the National Institute on Drug Abuse-modified Alcohol, Smoking, and Substance Involvement Screening Test 2 was used to assess alcohol-related problems and sports gambling frequency was assessed by a single item. Results: The cross-section of US adults (n = 2806) and oversample of sports-gambling adults (n = 1557) resulted in a total baseline sample of 4363 (mean [SD] age, 49.6 [16.2] years; 2243 men [51.4%] and 2120 women or nonbinary gender reported [48.6%]). Latent growth curve modeling revealed that alcohol problems decreased over time (slope = -0.059; 95% CI, -0.090 to -0.028). Sports gambling frequency did not show a significant trend over time (slope = -0.003; 95% CI, -0.053 to 0.047), though there was significant variance in this slope (variance = 0.024; 95% CI, 0.013 to 0.034). The trajectories of alcohol-related problems and sports gambling did not move independently, instead being highly positively correlated, suggesting that increases in one would correspond to increases in the other. Conclusions and Relevance: This study found that over time, the trajectory of sports gambling frequency was associated with the trajectory of alcohol-related problems. Screening and treatment interventions are recommended for sport gamblers who also drink concurrently, especially because this group appears to be at an elevated risk for developing greater alcohol-related problems over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".