Impacts of a Self-Help Treatment Program for Problem Gamblers
Bibliographic record
Abstract
Despite availability of treatments for gamblers, few at-risk and pathological gamblers seek help. Self-help treatments offer a private and personalized alternative that may appeal to gamblers who need help. Objective: This study examines the impact of the self-help treatment JEu me questionne (JMQ) on gambling behavior and severity, and reports participants’ satisfaction. Method: Forty-seven at-risk and pathological gamblers entered the program that involved a self-help treatment workbook and two motivational phone interviews. Results: Among the 32 gamblers who completed the program, results indicated a significant reduction in the number of pathological gambling diagnostic criteria. This gain was maintained at the one- and six-month follow-ups. Time gambling and money spent were also significantly lower post-treatment, but only a reduction in time spent gambling was maintained at follow-up. Participants reported high satisfaction with the program. The discussion raises clinical and theoretical implications of these findings. Malgré la disponibilité des traitements pour les joueurs, peu de joueurs à risque et pathologiques entreprennent une démarche d'aide. Les traitements auto-administrés (TAA) proposent une formule individualisée et peu intrusive ayant le potentiel de rejoindre les joueurs réticents afin qu'ils demandent de l'aide. Objectifs: Cette étude examine les impacts du TAA JEu me questionne (JMQ) sur les comportements de jeu et la sévérité des problèmes de jeu et documente la satisfaction des participants. Méthode: Quarante-sept joueurs à risque et pathologiques ont débuté le programme comprenant un manuel d'autotraitement et deux rencontres téléphoniques de type motivationnel. Résultats: Parmi les 32 joueurs qui ont complété le programme, les résultats indiquent une réduction significative du nombre de critères diagnostiques du jeu pathologique. Ce gain se maintient aux suivis 1 et 6 mois. Le temps consacré au jeu et l'argent misé diminuent également lors du post-traitement, mais seule la réduction du temps passé à jouer se maintient aux suivis. Les participants rapportent un taux élevé de satisfaction envers le programme. La discussion soulève les implications cliniques et théoriques de ces résultats.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".