Exposure Therapy for Gambling Disorder: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Purpose of Review Cognitive behaviour therapy is the gold standard for the treatment of gambling disorder. Obstacles remain regarding its efficacy, namely relapses and difficulty in implementing cognitive restructuring for some clients. Given these observations, behavioural interventions for gambling disorder, such as exposure therapy, which aims to decrease gambling craving, may be effective as a complementary or alternative intervention to cognitive behaviour therapy. This systematic review and meta-analysis aims to explore how exposure therapy for gambling disorder has been studied and to evaluate its efficacy. To answer these questions, 3406 studies, retrieved using PsycNet, Medline and Google Scholar, were screened. Recent Findings After two screenings, 13 papers were selected for the systematic review and five were statistically combined for the meta-analysis. Quantitative results support exposure therapy’s efficacy to decrease gambling craving at post-intervention (g = − 0.955) and at last follow-up (6 or 12 months; − 1.010). Results also show a large decrease in gambling severity as documented by screening instruments (− 1.087) as well as time spent gambling (− 2.136) at post-intervention. Furthermore, a large decrease in gambling measured via screening instruments (− 1.162) and erroneous beliefs (− 1.308) was found at last follow-up. Summary This is the first meta-analysis on behavioural exposure therapy for gambling disorder. Results support that exposure therapy reduces gambling cravings and severity, as well as time spent gambling and erroneous beliefs. These results are discussed in comparison to other therapeutic approaches and are interpreted according to the high risk of bias in included studies.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".