Responsible gambling: a synthesis of the empirical evidence
Bibliographic record
Abstract
Many jurisdictions around the world have implemented Responsible Gambling (RG) programs for the purpose of preventing gambling-related harms. Using a research synthesis strategy, this paper examines the extant peer-reviewed empirical evidence underpinning RG strategies. Instead of reporting all available studies and then discarding many on the ground of methodological flaws, we used the following a priori set of inclusion criteria: (1) All studies must have been conducted within real gambling environments with ‘real’ gamblers; and studies must have included at least one of the following elements: (2) a matched control or comparison group; (3) repeated measures; and (4) one or more measurement scales. The results revealed that only 29 articles met at least one of the methodological criteria. These empirical studies revealed five primary RG strategies. These findings have practical implications for evidence-based implementation of RG activities.
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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 | 0.051 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.044 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".