Evaluating the Effectiveness of Responsible Gambling Messages: A Rapid Evidence Assessment
Bibliographic record
Abstract
To minimize the harms associated with gambling, an array of responsible gambling (RG) messages has been developed to raise awareness of the risks of problem gambling and encourage safer gambling behaviors. However, evidence is limited as to the utility of RG messages to promote positive gambling-related beliefs and behaviors. In the current paper, we report the results of a Rapid Evidence Assessment (REA) of empirical research on RG messages. We identified 3200 unique articles published between 1890 and September 2024 using search terms related to RG messaging. Eighteen articles (containing 20 unique studies) met our inclusion criteria. Two general themes emerged: 1) RG message preferences among players and 2) RG message effectiveness. Specifically, players prefer self-appraisal messages, which were more effective in promoting RG behaviors compared to informative messages. Messages content also needs to be segmented (i.e., low-risk players prefer different types of messages than high risk players, such as highlighting player quizzes for low-risk players and helplines for high-risk players). Lastly, RG messages should be presented dynamically (e.g., pop-ups on an Electronic Gaming Machine: EGM). Results suggest a need for the gambling industry to adopt targeted, evidence-based RG messaging, as well as a need to engage in integrated knowledge mobilization, to more effectively promote RG. These findings underscore the importance of tailoring RG messages to player risk levels and preferences while leveraging dynamic delivery methods to maximize their effectiveness in promoting safer gambling behaviors and reducing harm.
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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 | 0.293 | 0.569 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.034 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| 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".