“Quitting gambling can help you with the relationships that matter the most to you”: An experimental comparison of positive emotional harm prevention (safer) gambling messages
Bibliographic record
Abstract
Objective: Australia was the first jurisdiction to replace gambling industry slogans such as “gamble responsibly” with independently-designed messages. One of the seven current Australian messages is a positive emotional message, intended to leverage positive feelings to enact behavioral change (“imagine what you could be buying instead”). This study sought to compare this and other proposed positive emotional messages against novel alternatives.Method: UK-based gamblers (N=4,204) rated 10 positive emotional messages on four Likert scales (e.g., “this message makes me want to gamble less”; “this message is relevant to me”) and provided free-text responses. Participants also completed the Problem Gambling Severity Index (PGSI), to explore how message ratings varied with levels of gambling harm.Results: Participants did not report disliking messages on average, and saw all messages as being relevant to people experiencing gambling harms. However, the average participant did not think that messages were relevant to them, and only six messages made the average participant want to gamble less. Two novel messages appeared the best overall when scores were averaged across these ratings. Messages appeared better suited to those who are experiencing gambling harms: participants with non-zero PGSI scores reported consequently wanting to gamble less, and messages appeared relevant to participants with PGSI scores of 8+. Analysis of free-text comments revealed that only one message yielded a positive average sentiment from participants, with a judgmental or guilt-inducing tone sometimes being perceived.Conclusions: Continual message design and evaluation can help inform the implementation of gambling harm prevention messages.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".