Cognitive-Behavioral Treatment for Gambling Harm: Umbrella Review and Meta-Analysis
Bibliographic record
Abstract
The aim of the current umbrella review and meta-analysis was to evaluate the methodological rigor of existing meta-analyses on cognitive-behavioral treatment (CBT) for gambling harm. The Cochrane Database of Systematic Reviews, PsycINFO, and PubMed were searched for meta-analyses of CBT for gambling harm among individuals aged 18 years and older. The search yielded five meta-analyses that met inclusion criteria, representing 56 unique studies and 5,389 participants. The methodological rigor for one meta-analyses was rated high, two were moderate, and two were critically low. Including only moderate- to high-quality meta-analyses, a robust variance estimation meta-analysis indicated that CBT significantly reduced gambling disorder severity (g = -0.91), gambling frequency (g = -0.52), and gambling intensity (g = -0.32) relative to minimal and no treatment control at posttreatment, suggesting 65%-82% of participants receiving CBT will show greater reductions in these outcomes than minimal or no treatment controls. Overall, there is strong evidence for CBT in reducing gambling harm and gambling behavior, and this evidence provides individuals, clinicians, managed care companies, and policymakers with clear recommendations about treatment selection.
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 | 0.053 | 0.136 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.024 | 0.047 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".