Clinical and public safety risks associated with cannabis legalization and frequency of cannabis use among forensic mental health patients
Bibliographic record
Abstract
BACKGROUND: There are ongoing concerns regarding the impact of Canada's cannabis legalization and commercialization on vulnerable persons such as those with serious forms of mental illness, including persons with schizophrenia-spectrum disorders and users of forensic mental health services. The primary objective of this study was to investigate the potential harms and mental health-related impacts associated with cannabis legalization on a sample of forensic patients in Ontario (N = 187). METHODS: Using a pseudo-prospective design, we investigated the frequency of cannabis use over a four-year period encompassing two years preceding and two years following the legislative change. We recorded clinical and public safety outcomes (i.e., mental health deterioration, length of stay in the forensic system, rates of hospital readmission, victimization and violence) over the same period to test relationships between these variables and rates of cannabis use. RESULTS: We found that one-third of patients either self-reported or were discovered, via urine testing, to have used cannabis over the study period. Frequency of use was lower in the pre-legalization period, and then gradually and significantly increased after legalization. Compared to patients with no cannabis use, those with one or more instances of use were more likely to be readmitted to hospital and had higher rated static risk factors for violence. However, there were no observed differences in the actual rate of violence between patients using and not using cannabis, nor differences in the rate of violence over time. Over half of the patients who used cannabis experienced a worsening of their mental health status in the week following use. CONCLUSIONS: Cannabis use among those with SMI is associated with adverse clinical outcomes. Results from this study suggest that the mental health burdens associated with cannabis use have risen in terms of delayed clinical recovery and progress through the forensic system since legalization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".