Prevention of Pathological Gambling: A Randomized Controlled Trial
Bibliographic record
Abstract
Although the gambling industry is expanding rapidly throughout North America and around the world, there are only a few empirically evaluated programs aimed at the prevention of pathological gambling (PG). The purpose of this study was to measure the effectiveness of a new prevention program aimed at PG. The Stop & Think! program was designed to teach at-risk video lottery terminal (VLT) gamblers cognitive restructuring and problem-solving skills that may help to prevent the development of PG. These skills were taught through a variety of methods - including an automated educational presentation, video and text vignettes, audio training tapes, and skill rehearsal. The program was evaluated using a randomized, 2-group experimental design with a wait-list control group and pre-, post-, and follow-up measures. Results indicated that, compared with the control group, the experimental group was less at risk for developing a gambling problem after the program. The experimental group endorsed fewer gambling-related cognitive distortions, engaged in less VLT gambling, and had lower scores on a measure of PG. The results of this study provide the basis for the implementation of the Stop & Think! program in the province of Prince Edward Island, Canada, and perhaps other jurisdictions too.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".