Win Big Fast! An Evaluation of Mobile Applications Available in Australia for Problem Gambling
Bibliographic record
Abstract
With the increase in availability of gambling applications (apps) for mobile phones, it has never been easier for individuals to access gaming systems. A proportion of these users will be affected by gambling disorder (GD). Traditional therapies for GD can be geographically and financially difficult to access. Mobile health apps can be useful for other addictions and provide another avenue of treatment for GD. Our objective in this study was to review the features, models of treatment, and aims of apps marketed to assist people in addressing their gambling. We searched the three largest app stores in Australia and performed a descriptive analysis based on the Mobile App Rating Scale of the apps purporting to be of assistance in managing GD or problem gambling. The number of apps available for addressing GD in Australia was vastly outnumbered by the number of apps for gambling or gaming. Apps that met the inclusion criteria most often aimed at total cessation of gambling, but did not use a recognizable therapeutic model. A majority of apps featured a single tool, most often a sober time tracker. Few of the apps were affiliated with existing services, and those that were tended to have a broader range of features and tools. Mobile apps present another way for individuals who are struggling with GD or problem gambling to access treatment. For apps to be effective, more attention needs to be paid to their design in order for them to be both useful and noticeable in the milieu of more invitingly designed apps that promote gambling.RésuméÉtant donné le nombre grandissant d’applications de jeux de hasard pour téléphone mobile, il n’a jamais été aussi facile d’accéder à des systèmes de jeu. Un certain nombre des utilisateurs de ces appareils développeront une dépendance au jeu (DJ). Les thérapies conventionnelles en matière de DJ peuvent être difficiles d’accès en raison de la distance géographique et de leur coût. Les applications mobiles dédiées à la santé, parfois pour traiter d’autres formes de dépendance, pourraient offrir des possibilités de traitement du jeu pathologique. Nous avons analysé les caractéristiques, les modèles de traitement et les objectifs des applications qui prétendent aider les individus à dominer leur DP. Nous avons fouillé les trois principales boutiques d’applications d’Australie à la recherche de tels produits, puis les avons soumis à une analyse descriptive fondée sur un Mobile App Rating Scale [échelle d’évaluation des applications mobiles]. Le nombre d’applications destinées au contrôle de la DJ est largement inférieur à celui des produits dédiés à la pratique des jeux de hasard et des jeux vidéo. Les applications retenues visent pour la plupart l’abandon définitif du jeu, sans reposer sur un modèle thérapeutique reconnaissable. La majorité comporte un seul et unique outil, soit un dispositif de minutage du temps passé sans jouer. Quelques-unes sont jumelées à des services existants; elles tendent à offrir un éventail plus grand de caractéristiques et d’outils. Les applications mobiles offrent aux personnes aux prises avec une dépendance au jeu une autre voie d’accès au traitement. Pour améliorer leur efficacité, toutefois, il faudra accorder une plus grande attention à leur conception et faire en sorte qu’elles se démarquent nettement des applications autrement plus attrayantes qui font la promotion du jeu.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".