Strategies to customize responsible gambling messages: a review and focus group study
Bibliographic record
Abstract
BACKGROUND: Responsible gambling messages are widely used as a tool to enable informed choice and encourage appropriate gambling behavior. It is generally accepted that gamblers have different levels of risk of developing gambling problems and require various harm minimization tools and resources. Therefore, it is reasonable to expect that responsible gambling messages should be customized and target specific groups of gamblers. This project aimed to understand hypothesized differences between cohorts of gamblers and receive qualitative feedback on archetypal targeted messages used to increase use of responsible gambling tools. METHODS: Focus groups were held to test messages for specific cohorts: young adults (18-24 years), seniors (60+ years), frequent gamblers (weekly), and gamblers of skill-based games (poker, sports betting). RESULTS: Cohorts exhibited different preferences and responses to message archetypes. Seniors preferred messages about limit setting, whilst young adults and frequent gamblers responded to messages about their own play and expertise. Skill game gamblers were interested in the odds of winning and their own outcomes over time. However, all groups agreed that using positive, non-judgmental language in messaging is important. CONCLUSIONS: This research makes an important contribution to the field by demonstrating that the wording of message content will likely influence the effectiveness of such messages differentially across various groups of gamblers for engaging gamblers in harm reduction tools. Guidance is provided on themes that can be used by public health marketers.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".