A Prospective Study of the Impact of Opening a Casino on Gambling Behaviours: 2- and 4-Year Follow-ups
Bibliographic record
Abstract
OBJECTIVE: It is widely believed that the rate of pathological gambling is related to the accessibility and availability of gambling activities. Few empirical studies have yet been conducted to evaluate this hypothesis. Using a longitudinal prospective design, the current study evaluates the impact of a casino in Canada's Hull, Quebec region. METHOD: A random sample of respondents from Hull (experimental group) and from Quebec City (comparison group) completed the South Oaks Gambling Screen (SOGS) and gambling- related questions before the opening of the Hull Casino (pretest), 1 year after the opening (posttest), and on follow-up at Years 2 and 4. RESULTS: Although, 1 year after the opening of the casino, we did observe an increase in playing casino games and in the maximum amount of money lost in 1 day's gambling, this trend was not maintained over time (2- and 4-year follow-ups). In the Hull cohort, the rate of at-risk and probable pathological gamblers and the number of criteria on the SOGS did not increase at the 2- and 4-year follow-ups. The residents' reluctance to open a local casino was generally stable over time following the casino's opening. CONCLUSION: The discussion raises different explanatory factors and focuses on the context of the Regional Exposure Model as a potentially more applicable measure of studying the expansion of gambling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".