Psychosocial Interventions for Individuals With Comorbid Psychosis and Substance Use Disorders: Systematic Review and Meta-analysis of Randomized Studies
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Substance use is highly prevalent among people with schizophrenia (SCZ) and related disorders, however, there is no broad-spectrum pharmacotherapy that concurrently addresses both addiction and psychotic symptoms. Psychosocial (PS) interventions, which have yielded promising results in treating psychosis and substance dependence separately, demonstrate potential but have not been systematically evaluated when combined. STUDY DESIGN: Systematic review and random-effects meta-analyses of randomized controlled trials (RCTs) investigating PS interventions for individuals with comorbid substance use and psychotic disorders, encompassing SCZ and schizophrenia spectrum disorders (SSD). We included relevant studies published from MEDLINE, PsycINFO, and Google Scholar through May 2023. STUDY RESULTS: We included 35 RCTs (5176 participants total; approximately 2840 with SSD). Intervention durations ranged from 30 min to 3 years. Meta-analysis did not identify a statistically significant pooled PS intervention effect on the main primary outcome, substance use (18 studies; 803 intervention, 733 control participants; standardized mean difference, -0.05 standard deviation [SD]; 95% CI, -0.16, 0.07 SD; I2 = 18%). PS intervention effects on other outcomes were also not statistically significant. Overall GRADE certainty of evidence was low. CONCLUSIONS: At present, the literature lacks sufficient evidence supporting the use of PS interventions as opposed to alternative therapeutic approaches for significantly improving substance use, symptomatology, or functioning in people with SCZ and related disorders. However, firm conclusions were precluded by low certainty of evidence. Further RCTs are needed to determine the efficacy of PS treatments for people with dual-diagnoses (DD), either alone or in combination with pharmacotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".