When should players be taught to gamble responsibly? Timing of educational information upregulates responsible gambling intentions
Bibliographic record
Abstract
Educating gamblers about responsible gambling (RG) practices (e.g. setting and adhering to a pre-set money limit) plays a central role in minimizing the harms associated with electronic gaming machine (EGM) play. However, little is known about when such educational information is best presented. Herein, using the principle of active learning, we tested the idea that players’ intentions to gamble responsibly will be heightened if RG educational information is provided in advance of (as opposed to following) a RG-related decision. To this end, a community sample of EGM players who were at a gaming venue (N = 98) were recruited to play an ostensibly real virtual reality slot machine and complete a survey prior to their planned gambling session. Participants were shown a RG-oriented educational animation just prior to initiating play or in advance of making a decision about whether to continue playing after their money limit was reached. As predicted, players who viewed the educational animation in advance of a RG-related decision about continuing play were more likely to express an intention to set a money limit in their upcoming gambling session at the gaming venue. Disordered gambling symptomatology moderated this effect—players low (compared to those high) in disordered gambling symptomatology expressed greater intention to set a money limit when the educational animation was viewed directly in advance of making a RG-related decision. Results suggest that learning RG actively (i.e. pairing RG education with its associated behavior, in vivo) can increase players’ intention to gamble responsibly.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".