Legal Status of Recreational Cannabis and Self-Reported Substitution of Cannabis for Opioids or Prescription Pain Medication in Canada and the United States
Bibliographic record
Abstract
Aims: With increased liberalization of cannabis policies in North America, there is growing interest in the use of cannabis to manage pain instead of opioids. The objectives of the study were to (1) examine the use of cannabis for pain relief in Canada and the United States (US) in 2018 and 2019; (2) examine the association between recreational cannabis laws and changes in the use of cannabis for pain relief, instead of opioids or prescription pain medication. Methods: Repeat cross-sectional survey data were used from Wave 1 and Wave 2 of the International Cannabis Policy Study conducted in 2018 and 2019 in Canada and the US. Respondents were recruited through commercial panels, aged 16–65, and had ever tried cannabis ( N = 44,119). Weighted binary logistic regression models examined the association between the legal status of recreational cannabis and cannabis use for pain relief instead of opioids or prescription pain medication ( n = 15,092). Results: Between 14–33% of cannabis consumers in Canada and the US reported using cannabis to manage headaches or pain. Of these consumers, 79% and 78% respondents in Canada; 80% and 83% in US illegal states; and 83% and 84% in US legal states, in 2018 and 2019, respectively, reported cannabis use for pain relief instead of opioids or prescription pain medication. There was little evidence of an association between the legal status of recreational cannabis and cannabis use for pain relief instead of opioids or prescription pain medication, among Canadian (AOR = 0.98, 95% CI: 0.78, 1.22) and US respondents (AOR = 1.11, 95% CI: 0.96, 1.28). Conclusions: Although substitution of cannabis for opioids or prescription pain medication is common among those who use cannabis for pain, there does not seem to be a significant difference according to cannabis legality. Future research should examine cannabis and opioid substitution using different research designs and time frames.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".