‘Chances are you’re about to lose’: new independent Australian safer gambling messages tested in UK and USA bettor samples
Bibliographic record
Abstract
Current industry-developed safer gambling messages such as ‘Take time to think’ and ‘Gamble responsibly’ have been criticized as ineffective slogans. As a result, Australia has recently introduced seven independently-developed safer gambling messages. The UK Government intends to introduce independently-developed messages from 2024 onwards, and this measure could be similarly appropriate for the US states where sports betting has been legalized and gambling advertising has become pervasive. Given this context, the current study recruited race and sports bettors from the UK and USA to elicit their perceptions of the seven Australian safer gambling messages. Participants (N = 1,865) rated on a Likert-scale seven newly introduced messages and two existing ones (‘Take time to think’ and ‘Gamble responsibly’) using seven evaluative statements. Participants also reported their levels of problem gambling severity. For most statements in both jurisdictions, the new messages performed significantly better than the existing ones. Specifically, the new messages were deemed more attention grabbing, applicable on a personal level, helpful to gamblers, and more likely to encourage cutbacks in gambling. The message that included a specific call to action (‘What are you prepared to lose today? Set a deposit limit’) was one of the best performing messages. Interaction effects observed in relation to jurisdiction, age, gender, and problem gambling severity were generally small enough to counteract the argument that different populations might benefit from substantially different messages. These findings add to previous research on the independent design of effective safer gambling 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".