A Critical Review of the Scholarly Discourse on Gambling Disorder Treatment
Bibliographic record
Abstract
This article presents a comprehensive review of the scholarly discourse on psychological and relational approaches to gambling disorder treatment. The article focuses on the “what” of knowledge production and treatment delivery by systematizing information on the types of scholarly articles that have been published in the English language; the treatment approaches that have been researched and discussed in the Anglophone literature; and the context of knowledge production over the past 50 years. The review includes 445 articles that present the findings of case studies and evaluations of disordered gambling interventions (k = 231), descriptive research (k = 49), meta-analyses (k = 10), and literature reviews and descriptions of novel approaches (k = 155). The findings show that Cognitive Behavioral Therapy (CBT), together with its constituent approaches, was the most discussed and researched approach to gambling disorder treatment in the period between late 1960s and the first half of 2019, covered by about 60% of the articles. Motivational Interviewing approaches were discussed in over one-fifth of the articles, whereas psychoanalytic and psychodynamic approaches accounted for under 10% of the articles. Roughly three-quarters of articles included in the review were published in North American and international journals. Our discussion situates these trends in critical discourses of the medicalization of mental health, dominance of Western mental health frameworks, and the politics of knowledge production.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".