Exploring the Effectiveness of an Intelligent Messages Framework for Developing Warning Messages to Reduce Gambling Intensity
Bibliographic record
Abstract
Warning messages are a common tool used in public health initiatives in an attempt to minimize consumer harm. Electronic gaming machines provide a unique opportunity to deliver messages that are personalized, that is, based on player behaviour, gambling history, and personal characteristics. This study explores whether messages that respond to player behaviour may be effective in reducing gambling intensity on the basis of the Intelligent Messages Framework (Langham, Thorne, Rockloff, & Donaldson, 2017). One-hundred and seventy-two participants (82 males, 90 females) from 20 to 88 years of age (M = 48.95, SD = 16.06) played a computerized gambling simulation. Participants were presented with a pop-up message following the 21st spin during game play, which varied according to message purpose (informative, self-monitoring, self-evaluative) and message frame (positive, challenging, negative). Results showed that female participants had faster betting speeds, greater betting persistence, and greater total losses in the negative, self-evaluative condition than in the other conditions. Findings suggest that messages need to be tailored appropriately to the consumer’s characteristics to be effective. Messages that do not consider the individual needs of the consumer may increase gambling intensity and therefore fail to be an effective harm-minimization tool. More sophisticated methods of delivering messages to consumers need to be developed and tested, particularly given that ineffective messages have the potential to be counterproductive in reducing gambling intensity.RésuméLes messages d’avertissement sont un outil souvent utilisé dans les initiatives de santé publique pour minimiser les préjudices causés aux consommateurs. Les machines de jeux électroniques offrent une possibilité unique de livrer des messages personnalisés, c’est-à-dire basés sur le comportement des joueurs, l’historique de jeu et les caractéristiques personnelles. Cette étude évalue si les messages qui répondent au comportement des joueurs peuvent être efficaces pour réduire l’intensité du jeu sur la base du cadre de travail de messages intelligents (Intelligent Messages Framework) (Langham, Thorne, Rockloff et Donaldson, 2017). Cent-soixante-douze participants (82 hommes, 90 femmes) âgés de 20 à 88 ans (M = 48,95, SD = 16,06) ont participé à une simulation de jeu informatisée. Les participants ont reçu un message contextuel à la suite du 21e tour de jeu, qui variait en fonction du but du message (informatif, auto-surveillance, auto-évaluation) et du cadre du message (positif, stimulant, négatif). Les résultats ont montré que les participantes avaient des vitesses de pari plus rapides, une plus grande persistance de paris et des pertes totales plus importantes dans la condition négative d’auto-évaluation, comparée à d’autres conditions. Les résultats indiquent que pour être efficaces, les messages doivent être adaptés de manière appropriée en fonction des caractéristiques du consommateur. Les messages qui ne tiennent pas compte des besoins individuels du consommateur peuvent augmenter l’intensité du jeu et ainsi n’être d’aucune utilité pour minimiser les dommages. Il est nécessaire de concevoir et de tester des méthodes plus sophistiquées pour livrer des messages aux clients, d’autant plus que les messages inefficaces peuvent être contre-productifs pour réduire l’intensité du jeu.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".