Efficacy of a Voluntary Self-exclusion Reinstatement Tutorial for Problem Gamblers
Bibliographic record
Abstract
Voluntary self-exclusion programs allow gamblers to voluntarily be denied access to gambling venues for an agreed upon period. Many people who self-exclude decide to return to gambling venues after the exclusion period has ended, however people who reinstate may be at risk for the recurrence of gambling problems. This study was designed to determine the efficacy of a tutorial created with the intent of reducing the risk of harm to those who reinstate. People who wished to be reinstated were asked to complete a survey on gambling related issues and then watch the tutorial video. An online video-based tutorial designed to reduce gambling related harm and to provide information about treatment services was developed. The control group (N = 131) consisted of people who reinstated in the year prior to the implementation of the online tutorial. The experimental intervention group (N = 104) were those who reinstated after the implementation of the online tutorial. There was a significant decrease in gambling and problem gambling comparing pre-exclusion to during exclusion in both the experimental and control group. Furthermore, this drop in gambling problem was sustained for 6-months and 12-months after reinstatement. However, no main effect or interaction was found that supported the efficacy of the tutorial. Self-exclusion by itself was associated with a sustained reduction in problem gambling. There was no significant evidence that the educational tutorial had any additional impact on the reinstatement process.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".