Demographic, gambling, and psychological characteristics of sports bettors in a stratified sample of adults who gamble regularly.
Bibliographic record
Abstract
OBJECTIVE: Sports betting accounts for the greatest proportion of online gambling behavior globally and has been linked to various harms. Few studies have examined the average sports bettor profile using stratified samples of adults who gamble regularly. The present study builds upon existing research on the demographic variables of sports bettors and provides an in-depth examination of the clinical and gambling-related factors associated with sports betting. METHOD: Participants (N = 10,039; 53.6% female) consisted of a stratified sample of Canadian adults who endorsed participating in gambling at least once per month in the past year. Participants completed standardized measures assessing demographics, gambling behavior, problem gambling severity, gambling-related harms, gambling motives, and psychological characteristics (e.g., substance use, mental health). RESULTS: About 1,816 participants (18.1%) reported engaging in sports betting in the past year. Sports bettors tended to be younger, male, and employed full-time compared to nonsports bettors. Sports bettors endorsed different patterns of clinical comorbidities and greater substance use. Sports betting was also associated with unique gambling motives and greater time and money spent on gambling. Among participants who endorsed problem gambling, sports betting was associated with greater impulsivity and likelihood of using illicit substances while gambling. CONCLUSIONS: The results highlight the characteristics of individuals who bet on sports, as well as the characteristics of sports bettors with problem gambling which may help to inform the development of targeted prevention and intervention efforts to mitigate the potential harms of sports betting. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".