Effects of a workplace prevention program for problem gambling: A cluster-randomized controlled trial
Bibliographic record
Abstract
Background and Aims: This study evaluated whether a preventive intervention program for problem gambling would increase managers' inclination to act when concerned about gambling in the workplace. Design: Cluster randomized controlled trial. Ten workplaces were randomized to either intervention or control condition. Participants: At the 12-month endpoint, there were n = 136 managers and n = 1594 subordinates in the intervention group, and n = 137 managers and n = 1150 subordinates in the waitlist group. Intervention: The intervention consisted of (1) six hours of skill-development training for managers regarding gambling, problem gambling, gaming, and harmful use of psychoactive drugs, and (2) six to eight hours of assistance in developing or improving workplace gambling policy. Measurements: The primary outcome was the managers' self-rated (on a 1 to 10 scale) inclination to act when concerned about an employee's problem gambling 12 months after baseline. Findings: The between -group difference in the managers' inclination for the full intervention group (M = 8) and the control group (M = 7.4) was not significant at the 12-month follow-up, but it was when only including managers who attended the skill development training (M = 8.2), d = 0.31, p = .04. Conclusion: A workplace prevention program aimed to increase managers' inclination to act when they are concerned regarding an employee's gambling resulted in statistically significant changes for those who attended training, but not for the whole intervention group when non-attendees were included.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".