The Prevalence of Comorbid Personality Disorders in Treatment-Seeking Problem Gamblers: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The aim of this study was to systematically review and meta-analyze the prevalence of comorbid personality disorders among treatment-seeking problem gamblers. Almost one half (47.9%) of problem gamblers displayed comorbid personality disorders. They were most likely to display Cluster B disorders (17.6%), with smaller proportions reporting Cluster C disorders (12.6%) and Cluster A disorders (6.1%). The most prevalent personality disorders were narcissistic (16.6%), antisocial (14.0%), avoidant (13.4%), obsessive-compulsive (13.4%), and borderline (13.1%) personality disorders. Sensitivity analyses suggested that these prevalence estimates were robust to the inclusion of clinical trials and self-selected samples. Although there was significant variability in reported rates, subgroup analyses revealed no significant differences in estimates of antisocial personality disorder according to problem gambling severity, measure of comorbidity employed, and study jurisdiction. The findings highlight the need for gambling treatment services to conduct routine screening and assessment of co-occurring personality disorders and to provide treatment approaches that adequately address these comorbid conditions.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".