Prevalence of psychiatric co-morbidity in treatment-seeking problem gamblers: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this paper was to systematically review and meta-analyse the prevalence of co-morbid psychiatric disorders (DSM-IV Axis I disorders) among treatment-seeking problem gamblers. METHODS: A systematic search was conducted for peer-reviewed studies that provided prevalence estimates of Axis I psychiatric disorders in individuals seeking psychological or pharmacological treatment for problem gambling (including pathological gambling). Meta-analytic techniques were performed to estimate the weighted mean effect size and heterogeneity across studies. RESULTS: Results from 36 studies identified high rates of co-morbid current (74.8%, 95% CI 36.5-93.9) and lifetime (75.5%, 95% CI 46.5-91.8) Axis I disorders. There were high rates of current mood disorders (23.1%, 95% CI 14.9-34.0), alcohol use disorders (21.2%, 95% CI 15.6-28.1), anxiety disorders (17.6%, 95% CI 10.8-27.3) and substance (non-alcohol) use disorders (7.0%, 95% CI 1.7-24.9). Specifically, the highest mean prevalence of current psychiatric disorders was for nicotine dependence (56.4%, 95% CI 35.7-75.2) and major depressive disorder (29.9%, 95% CI 20.5-41.3), with smaller estimates for alcohol abuse (18.2%, 95% CI 13.4-24.2), alcohol dependence (15.2%, 95% CI 10.2-22.0), social phobia (14.9%, 95% CI 2.0-59.8), generalised anxiety disorder (14.4%, 95% CI 3.9-40.8), panic disorder (13.7%, 95% CI 6.7-26.0), post-traumatic stress disorder (12.3%, 95% CI 3.4-35.7), cannabis use disorder (11.5%, 95% CI 4.8-25.0), attention-deficit hyperactivity disorder (9.3%, 95% CI 4.1-19.6), adjustment disorder (9.2%, 95% CI 4.8-17.2), bipolar disorder (8.8%, 95% CI 4.4-17.1) and obsessive-compulsive disorder (8.2%, 95% CI 3.4-18.6). There were no consistent patterns according to gambling problem severity, type of treatment facility and study jurisdiction. Although these estimates were robust to the inclusion of studies with non-representative sampling biases, they should be interpreted with caution as they were highly variable across studies. CONCLUSIONS: The findings highlight the need for gambling treatment services to undertake routine screening and assessment of psychiatric co-morbidity and provide treatment approaches that adequately manage these co-morbid disorders. Further research is required to explore the reasons for the variability observed in the prevalence estimates.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.010 | 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) | 0.003 | 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".