A Critical Review of the Scholarly Discourse on Gambling Disorder Treatment
Bibliographic record
Abstract
This article presents a critical systematic review of the literature on disordered gambling treatment, with a focus on the “how” of treatment delivery. A review of six peer-reviewed research databases was performed, along with hand searches of select journals. Peer-reviewed articles that discussed or evaluated psychological and relational treatments of gambling disorder were selected for a review and coded independently by all members of the research team. The sample for this study included 445 articles that were published in the English language over the past 50 years, through June 2019. The sample included not only evaluations and case studies (k = 231) but also descriptive research (k = 49), meta-analyses (k = 10), and literature reviews (k = 155). The results showed that face-to-face, professionally facilitated treatment of individuals has remained the primary focus of problem gambling literature during the period under study. That said, a number of alternative treatment modalities have emerged, particularly in the last two decades. This includes increased reliance on technology (i.e., internet and telephone/text) as an adjunct to face-to-face treatment or as a means for delivering stand-alone professionally facilitated or self-directed interventions. Our discussion includes the benefits of these approaches as reflected in the literature while also situating findings within discourses on Western-dominated trends toward the use of technology, prioritization of efficiency, and individual focus in mental health treatment.
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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 | 0.027 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".