Cheating and stealing to finance gambling: analysis of screening data from a problem gambling self-help program
Bibliographic record
Abstract
Previous studies have suggested strongly that early engagement in gambling anticipates severe gambling problems. Problem gambling and gambling addiction are linked to financial difficulties, depression and weakened life control. One social consequence of excessive gambling is property crime. In this study, we analyze screening data (N = 1573) from a problem gambling self-help program to locate predictors of such criminal behaviour. We applied logistic regression to determine the relationship between problem gambling and both reported cheating and stealing. Our objective was to create an empirically-based model of the different risk factors related to such criminogenic gambling. Our models suggest that self-reported gambling-related cheating and stealing is related to young age, low education, low income, a high rate of depression, a long history of problem gambling, and negative subjective perception of one’s financial situation.RésuméDes études antérieures ont confirmé qu’une participation précoce à des jeux d’argent prédit de graves problèmes de jeu. Le jeu compulsif et la dépendance au jeu sont liés aux difficultés financières, à la dépression et à un faible contrôle sur la vie. Une conséquence sociale du jeu excessif est la criminalité contre les biens. Dans cette étude, nous analysons les données de dépistage (N = 1573) d’un programme d’auto-assistance sur le jeu problématique pour trouver des prédicateurs d’un tel comportement criminel. Nous avons appliqué la régression logistique pour déterminer la relation entre le jeu problématique et la tricherie et le vol rapportés. Notre objectif était de créer un modèle empirique des différents facteurs de risque liés à ces jeux criminogènes. Nos modèles suggèrent que la tricherie et le vol autodéclarés attribuables au jeu sont liés au jeune âge, à un faible niveau de scolarité, à un faible revenu, à un taux élevé de dépression, à une longue histoire de jeu compulsif et à une perception subjective négative de sa situation financière.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".