Prevalence of schizophrenia spectrum and other psychotic disorders in problem gambling: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: High rates of psychiatric comorbidities have been found in people with problem gambling (PBG), including substance use, anxiety, and mood disorders. Psychotic disorders have received less attention, although this comorbidity is expected to have a significant impact on the course, consequences, and treatment of PBG. This review aimed to estimate the prevalence of psychotic disorders in PBG. METHODS: Medline (Ovid), EMBASE, PsycINFO (Ovid), CINAHL, CENTRAL, Web of Science, and ProQuest were searched on November 1, 2023, without language restrictions. Studies involving people with PBG and reporting the prevalence of schizophrenia spectrum and other psychotic disorders were included. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist for systematic reviews of prevalence data. The pooled prevalence of psychotic disorders was calculated using a random effects generalized linear mixed model and presented with forest plots. RESULTS: = 88%). A lower prevalence was found in surveyed/recruited populations, compared with treatment-seeking individuals and register-based studies. No differences were found for factors such as treatment setting (inpatient/outpatient), diagnoses of psychotic disorders (schizophrenia only/other psychotic disorders), and assessment time frame (current/lifetime). The majority of included studies had a moderate risk of bias. CONCLUSIONS: These findings highlight the relevance of screening problem gamblers for schizophrenia spectrum and other psychotic disorders, as well as any other comorbid mental health conditions, given the significant impact such comorbidities can have on the recovery process.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| 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".