Bibliographic record
Abstract
Problem gamblers face numerous barriers to intervention and support, such as shame and stigma, need for control, and lack of resources. Fortunately, digital health has paved the way for private, autonomous, and highly accessible interventions for problem gambling. Mobile applications (apps) are a part of the digital health platform; however, few apps are available, and a review has not been undertaken. This study had one simple aim: to review the health apps for problem gambling available in Australia from Google Play and Apple iTunes. Focus was given to, among other elements, cost, update recency, popularity, and functions of the apps. In January 2020, 17 health apps for problem gambling were identified and data were extracted. The investigation showed that the apps are generally free or low cost and are not popular in terms of downloads and ratings. In most cases, months or years had passed since previous updates, and the apps had a small number of functions with little variance in the types provided. However, many of the functions are viewed positively by problem gamblers and professionals involved in problem gambling research and intervention. Overall, although the limited range of health apps for problem gambling available in Australia provides a foundation for intervention, there is room for improvement in the quality and range of in-app functions, which may in turn have positive effects on popularity. Further, a greater number of apps may benefit users by encouraging price competitiveness and regular app updates.RésuméLes joueurs compulsifs font face de nombreux obstacles à l’intervention et au soutien, notamment la honte et la stigmatisation, le besoin de contrôle et le manque de ressources. Heureusement, la santé numérique leur a ouvert la voie aux interventions privées, autonomes et très accessibles. La plateforme de santé numérique comporte des applications mobiles, mais celles-ci sont peu nombreuses et n’ont pas fait l’objet d’analyses. Cette étude avait un objectif simple : examiner les applications du domaine de la santé destinées aux joueurs compulsifs et offertes en Australie à partir de Google Play et d’Apple iTunes. Nous nous sommes concentrés notamment sur le coût, la récence de la mise à jour, la popularité et les fonctions des applications. En janvier 2020, 17 applications du domaine de la santé destinées aux joueurs compulsifs ont été repérées et les données ont été extraites. L’analyse montre que les applications sont généralement gratuites ou à faible coût, et ne sont pas populaires sur le plan des téléchargements et des évaluations. Dans la plupart des cas, elles n’avaient pas été mises à jour depuis des mois ou des années, et offraient un petit nombre de fonctions dont le type variait peu. Toutefois, un grand nombre des fonctions sont considérées de manière positive par les joueurs compulsifs et les professionnels de la recherche et de l’intervention dans le domaine du jeu compulsif. Dans l’ensemble, bien que la gamme restreinte d’applications de santé destinées aux joueurs compulsifs et accessibles en Australie jette les bases de l’intervention, il y a place à l’amélioration de la qualité et des fonctions de ces applications, ce qui pourrait les rendre plus populaires. En outre, l’augmentation du nombre d’applications pourrait être profitable aux utilisateurs en favorisant la concurrence des prix et les mises à jour régulières.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".