A Review of Sports Wagering: Prevalence, Characteristics of Sports Bettors, and Association with Problem Gambling
Bibliographic record
Abstract
Given significant technological advances, the U.S. Supreme Court ruling in 2018 permitting U.S. states to offer and regulate sports wagering, and multiple international governments already regulating and licensing sports wagering operators, sports wagering will likely continue to grow exponentially. This expanding landscape of sports wagering may pose public health problems. This literature review provides a description of our current knowledge of sports gambling behaviour among adults, adolescents, and athletes, including prevalence rates and factors associated with problem gambling sports bettors. We highlight new issues that are surfacing, particularly the interaction between online betting, sports viewing, live betting, mobile technology, and sports fantasy gambling. We also address future research directions, including the need for longitudinal studies to clarify factors that contribute to the onset and maintenance of sports-related problem gambling, to examine the impact of major league sports leagues and professional teams that partner with gambling operators and casinos on gambling behaviour, and the need to assess public policy and treatment approaches.RésuméCompte tenu des progrès technologiques importants, de la décision rendue par la Cour suprême des États-Unis en 2018 autorisant les États américains à proposer et à réglementer les paris sportifs et des multiples réglementations gouvernementales internationales les autorisant, les paris sportifs continueront probablement de croître de manière exponentielle. Or, ce domaine de paris sportifs en pleine expansion peut poser des problèmes de santé publique. Cet examen de la littérature contient une description de nos connaissances actuelles sur le comportement au jeu sportif chez les adultes, les adolescents et les athlètes, notamment les taux de prévalence et les facteurs associés aux parieurs sportifs du jeu problématique. Nous mettons en lumière des problèmes émergents, notamment l’interaction entre les paris en ligne, l’écoute de joutes sportives, les paris en direct, la technologie mobile et les jeux de hasard fantastiques. Y sont également abordés l’orientation des recherches futures, notamment la nécessité d’études longitudinales visant à clarifier les facteurs qui contribuent aux débuts et à la poursuite du jeu compulsif lié au sport, un examen de l’impact sur le comportement de jeu des collaborations entre des ligues sportives majeures et des équipes professionnelles et des opérateurs de jeux et des casinos, ainsi que les secteurs d’intérêt concernant les politiques publiques et le traitement.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".