Examining the Relationship Between Cannabis Use and Mood, Anxiety, and Psychotic Symptoms in Psychiatric Patients with Severe Concurrent Mental Health and Substance Use Disorders Before and After Recreational Cannabis Legalization in Canada
Bibliographic record
Abstract
Objective: The relationship between cannabis use and mental health has garnered significant attention in recent decades. However, studies have largely been in general populations or in countries in which recreational cannabis use is illegal. Method: The current cross-sectional study examines the relationship between cannabis use, mood disorders, anxiety, and psychosis in an inpatient psychiatric population with severe concurrent mental health and substance use disorders, exploring the potential moderating effect of the legalization of recreational cannabis in Canada. Results: Cannabis use compared to non-use was associated with higher self-reported depression, anxiety, and psychotic symptoms but was not associated with diagnosis of a mood, anxiety, or psychotic disorder. Frequency of cannabis use was unrelated to mental health outcomes, but age of first use was negatively associated with self-reported psychoticism symptoms. There were some significant associations between recreational cannabis legalization and mental health, but legalization was largely unrelated to outcomes. There were also some significant differences by demographics. Conclusions: While findings are relatively consistent with prior literature, some significant associations differed, suggesting the importance of examining concurrent disorder patients as a unique population when examining relationships between cannabis use and mental health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".