Contingency Management for Treatment of Cannabis Use Disorder in Co-Occurring Mental Health Disorders: A Systematic Review
Bibliographic record
Abstract
Amongst individuals with a mental health disorder, a comorbid diagnosis of cannabis use disorder (CUD) is associated with numerous adverse consequences, including more severe symptom profiles, poorer treatment response, and reduced psychosocial functioning. Contingency management (CM), a method to specifically reinforce target behavior attainment (e.g., substance use abstinence), may provide an effective intervention in treating cannabis use in patients with a dual diagnosis of CUD and a mental health disorder. A systematic search examining the effects of CM on cannabis use, clinical, cognitive, and psychosocial outcomes in patients with a mental health disorder on PubMed, PsycINFO, and EMBASE databases up to November 2022 was performed. Six studies met inclusion criteria for our review. We found CM to be efficacious in producing cannabis use reductions and abstinence amongst individuals with a psychotic-spectrum or major depressive disorder. Additional longitudinal studies with larger sample sizes, other psychiatric populations, and longer follow-up periods are needed to evaluate the sustained effects of CM.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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".