Electronic gaming machine accessibility and gambling problems: A natural policy experiment
Bibliographic record
Abstract
Background: Electronic gaming machines (EGMs) are one of the most harmful forms of gambling at an individual level. It is unclear whether restriction of EGM functions and accessibility results in meaningful reductions in population-level gambling harm. Methods: A natural policy experiment using a large (N = 15,000) national dataset weighted to standard population variables was employed to compare estimates of gambling problems between Australian residents in Western Australia (WA), where EGMs are restricted to one venue and have different structural features, to residents in other Australian jurisdictions where EGMs are widely accessible in casinos, hotels and clubs. Accessibility of other gambling forms is similar across jurisdictions. Results: Gambling participation was higher in WA, but EGM participation was approximately half that of the rest of Australia. Aggregate gambling problems and harm were about one-third lower in WA, and self-reported attribution of harm from EGMs by gamblers and affected others was 2.7× and 4× lower, respectively. Mediation analyses found that less frequent EGM use in WA accounted for the vast majority of the discrepancy in gambling problems (indirect path = -0.055, 95% CI -0.071; -0.038). Moderation analyses found that EGMs are the form most strongly associated with problems, and the strength of this relationship did not differ significantly across jurisdictions. Discussion: Lower harm from gambling in WA is attributable to restricted accessibility of EGMs, rather than different structural features. There appears to be little transfer of problems to other gambling forms. These results suggest that restricting the accessibility of EGMs substantially reduces gambling harm.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".