Who makes in-play bets? Investigating the demographics, psychological characteristics, and gambling-related harms of in-play sports bettors
Bibliographic record
Abstract
Background and aims: Sports betting has increased markedly in recent years, in part due to legislative changes and the introduction of novel forms of sports betting (e.g., in-play betting). Some evidence suggests that in-play betting is more harmful than other types of sports betting (i.e., traditional and single-event). However, existing research on in-play sports betting has been limited in scope. To address this gap, the present study examined the extent to which demographic, psychological, and gambling-related constructs (e.g., harms) are endorsed by in-play sports bettors relative to single-event and traditional sports bettors. Methods: Sports bettors (N = 920) aged 18+ from Ontario, Canada completed an online survey containing self-report measures of demographic, psychological, and gambling-related variables. Participants were classified as either in-play (n = 223), single-event (n = 533), or traditional bettors (n = 164) based on their sports betting engagement. Results: In-play sports bettors reported higher problem gambling severity, endorsed greater gambling-related harms across several domains, and reported greater mental health and substance use difficulties compared to single-event and traditional sports bettors. There were generally no differences between single-event and traditional sports bettors. Discussion: Results provide empirical support for the potential harms associated with in-play sports betting and inform our understanding of who may be at risk for increased harms associated with in-play betting. Conclusions: Findings may be important for the development of public health and responsible gambling initiatives to reduce the potential harms of in-play betting, particularly as many jurisdictions globally move towards legalization of sports betting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".