Potential Clinical Applications of Responsible Gambling
Bibliographic record
Abstract
Responsible gambling tools are designed to help people gamble within their means. Empirical assessments have demonstrated their effectiveness in preventing problematic play among recreational gamblers. Herein, we contend that responsible gambling tools may also have clinical utility. In this light, we review some of the available responsible gambling tools and discuss why and under what conditions each may be appropriate in a clinical setting. In doing so, we point to areas that will benefit from empirical research with clinical populations. First, we argue that education-based tools may be used to undermine the client's cognitive distortions and promote adherence to a pre-set limit. Second, we discuss monetary and time limit setting tools as an effective means to teach clients with moderation goals (in contrast to abstinence goals) how to minimize excessive play. Lastly, we suggest that personalized feedback (e.g., player account data or personalized gambling behaviour reports) provides a fairly unbiased assessment of the client's gambling activities that can facilitate discussions about the future course of treatment. Due to the paucity of attention focused on the clinical applications of responsible gambling tools, our assertions are guarded, but nonetheless highlight the need for research in this domain. Des outils pour le jeu responsable sont développés dans le but d'aider les gens à jouer tout en respectant leur limite. Des évaluations empiriques ont démontré l'efficacité de ces outils à prévenir la problématique du jeu de hasard et d'argent parmi les joueurs récréatifs. Dans cet article, nous alléguons que les outils pour le jeu responsable peuvent aussi avoir une utilité clinique. À la lumière de cette affirmation, nous passons en revue quelques-uns des outils pour le jeu responsable et discutons pourquoi et sous quelles conditions chaque outil peut être approprié en milieu clinique. Ce faisant, nous soulignons les domaines dans lesquels des travaux de recherche empirique pour les populations cliniques sont requis. Premièrement, nous avançons que les outils éducatifs peuvent être utilisés pour miner les distorsions cognitives des patients et promouvoir l'adhésion à une limite pré-déterminée. Deuxièmement, nous discutons l'utilisation d'outils pour fixer des limites monétaires et de temps comme moyen efficace d'enseigner aux clients qui ont des objectifs de modération (contrairement aux objectifs d'abstinence) comment minimiser le jeu excessif. Finalement, nous suggérons que la rétroaction personnalisée (p.ex.: les données sur le compte du joueur ou un rapport personnalisé sur les comportements de jeu) fournit une évaluation plutôt non biaisée des activités de jeu du client qui peut faciliter les discussions sur le traitement à venir. En raison du peu d'intérêt porté sur l'application clinique des outils pour le jeu responsable, nos affirmations sont prudentes, mais néanmoins soulignent le besoin de réaliser davantage de recherche dans ce domaine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".