Cannabis-Based Reductions in Opioid-Related Harms: Population-Based Observational Meta-Analysis
Bibliographic record
Abstract
Abstract Background: There is currently much debate around harms associated with easier access to cannabis. Yet, surveys of medical marijuana patients and recreational users in Canada and the USA observed that they prevalently (25% to 50%) substitute cannabis for alcohol, opioids, and other drugs, suggesting reduced harms. This synthetic study focused on arguably the most harmful substance opioids, as the growing toll of opioid-related morbidity and mortality requires harm reduction-based interventions. Methods: Broad keyword searches of interdisciplinary research databases between 2010 and 2020 retrieved 11 studies. Their outcomes were synthesized with a sample-weighted meta-analysis that compared opioid-related outcomes before and after marijuana legalization in states that legalized marijuana versus those that had not. Results: All but one of the primary study outcomes supported the harm reduction hypothesis. While controlling for typically 10 to 15 state-level differences, risks associated with opioid use diminished by 8% (RR = 0.92 [95% CI 0.91, 0.93] after legalization, 7% after medical marijuana legalization and 35% after recreational marijuana legalization (p < .05). Opioid prescriptions decreased by 8% and opioid overdose mortality diminished by 25% after medical marijuana legalization; both were even further diminished after recreational marijuana use was legalized. Conclusion: The potential human and policy significances are clear, suggesting that such legislation profoundly affects tens of thousands to millions of physicians, patients and addicts in protective ways. All of the studies, thus far, have been state-level ecological analyses. Future individual-level analyses in Canada and the USA ought to be accomplished to replicate (or refute) this study's estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.047 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".