Marijuana Legalization and Opioid Use Disorder in Ontario, Canada, From 2015 to 2021
Bibliographic record
Abstract
This study examined the trends in opioid-related hospitalizations and deaths in Ontario, Canada, from 2015, to 2021, with a particular focus on the periods before and after cannabis legalization. The social problem addressed was the ongoing opioid crisis, exacerbated by rising opioid-related health issues. Grounded in the harm reduction theory, this research explored whether cannabis legalization could serve as a substitute to mitigate opioid use disorder. Data were extracted from the Discharge Database, Emergency Department Database, National Ambulatory Care Reporting System, and Hospital Morbidity Database. Analysis using the generalized estimation equation (GEE) method revealed a significant increase in opioid-related hospitalizations, which rose from a mean of 405.56 (SD = 38.67) in 2015 to 1486.33 (SD = 49.803) in 2021, representing 366% increase. The hospitalization rate notably increased twofold in 2020 and threefold in 2021 compared to 2015. A one-way ANOVA demonstrated a statistically significant effect of time on both hospitalizations, F(6,65) = 37.67, p < 0.001, and deaths, F(6,65) = 8.67, p < 0.001. The GEE analysis indicated a significant rise in monthly hospitalizations from the illegal to the legal cannabis period (B = 61.99, SE = 6.37, p < 0.001), along with significant yearly increases in opioid-related deaths from 2015 to 2021. These findings suggest that opioid-related health issues intensified during the cannabis legalization era, though the specific impact of the COVID-19 pandemic on these trends remains unclear. The study’s implications for social change include insights into the potential role of cannabis legalization as a harm reduction strategy, which could inform policies aimed at addressing the opioid crisis.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".