Problem gambling in psychotic disorders: A systematic review and meta‐analysis of prevalence
Bibliographic record
Abstract
INTRODUCTION: Problem gambling (PBG) is more common in people with mental health disorders, including substance use, bipolar, and personality disorders, than in the general population. Although individuals with psychotic disorders might be expected to be more vulnerable to PBG, fewer studies have focused on this comorbidity. The aim of this review was to estimate the prevalence of PBG in people with psychotic disorders. METHODS: Medline (Ovid), EMBASE, PsycINFO (Ovid), CINAHL, CENTRAL, Web of science, and ProQuest were searched on November 1, 2023, without language restrictions. Observational and experimental studies including individuals with psychotic disorders and reporting the prevalence of PBG were included. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal for systematic reviews of prevalence data. The pooled prevalence of PBG was calculated using a fixed effects generalized linear mixed model and presented through forest plots. RESULTS: = 69%). A lower prevalence was found in studies with a low risk of bias (5.6%; 95% CI = 4.4%-7.0%) compared with studies with a moderate risk of bias (10.4%; 95% CI = 9.2%-11.7%). Different methods used to assess PBG also contributed to the heterogeneity found. CONCLUSION: This meta-analysis found substantial heterogeneity, partly due to the risk of bias of the included studies and a lack of uniformity in PBG assessment. Although more research is needed to identify those at increased risk for PBG, its relatively high prevalence warrants routine screening for gambling in clinical practice.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".