How do academics, regulators, and treatment providers think that safer gambling messages can be improved?
Bibliographic record
Abstract
Safer gambling messages are a common public health intervention for gambling, and yet there is little evidence to support the variety of messages that are in widespread use. This paper thematically analysed the perspectives of 21 participants ⎼ including academics, regulators and treatment providers ⎼ regarding the design characteristics of safer-gambling messages with the goal to improve on those already being used. The focus groups were semi-structured and discussed exemplar messages based on five areas of previous gambling research: teaching safer gambling practices, correcting gambling misperceptions, boosting conscious decision making, norm-based messages, and emotional messages. Five themes were supported by the three focus groups, including that messages: may be insufficient to change behaviour; should respect the diversity amongst gamblers; should not contribute to gambling stigma; should provide norm-based information thoughtfully; and should trigger only positive and not negative emotions. These findings can be useful in developing messages that are based on themes endorsed by experts as being relevant to the design of effective safer-gambling messages. Generating a pool of messages that are evidence based is likely to improve on current messages, thus serving as a useful public health tool for promoting safer-gambling involvement.
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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 | 0.044 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".