The relationship between casino proximity and problem gambling
Bibliographic record
Abstract
Will increased casino proximity lead to, or correlate with, an increased prevalence of problem gambling? This study aims to address this research question by conducting a systematic review in the potential relationship between casino proximity and problem gambling. Keyword searches are conducted in PubMed and PsychINFO databases. Twelve studies, which were all from North America, were identified. Among the eight cross-sectional studies identified, correlations with statistical significance were demonstrated in five studies, indicating that casino proximity does have a role in problem gambling, but such correlations were not evident in the other three studies. Four longitudinal studies investigating the influence of new casino establishment on problem gambling were reported. The grand opening of a new casino resulted in increased casino gambling activities and problem gambling among local residents within 1 year, according to the studies conducted in Niagara Falls and Hull area, Canada. However, conflicting result was again observed in Windsor, Canada, as there was no significant increase in problem gambling within 1 year of new casino establishment. In addition, 2- and 4-year follow-up study in Hull area, Canada, showed that the rate of problem gambling did not increase, compared with those obtained before the casino establishment. The current data available from literature indicates that the relationship between casino proximity and problem gambling is still controversial, and remains to be established until more data are available, especially in Asian countries.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".