Cannabis use in patients treated for opioid use disorder pre- and post-recreational cannabis legalization in Canada
Bibliographic record
Abstract
BACKGROUND: As the legalization of recreational cannabis becomes more widespread, its impact on individuals with substance use disorders must be studied. Amidst an ongoing opioid crisis, Canada's legalization of recreational cannabis in October 2018 provides an important setting for investigation. We examined changes to cannabis use patterns in patients receiving medication-assisted treatment (MAT) for opioid use disorder (OUD) following legalization. METHODS: This study includes cross-sectional data from 602 participants recruited 6 months pre-legalization and 788 participants recruited 6 months post-legalization, providing information on cannabis use. Regression analysis was used to estimate the association between legalization and cannabis use patterns. We collected longitudinal urine drug screens (UDSs) detecting cannabis-metabolites for 199 participants recruited pre-legalization and followed prospectively post-legalization. Conditional logistic regression was used to assess the association between legalization and UDS results. RESULTS: Past-month cannabis use was self-reported by 54.8 and 52.3% of participants recruited pre- and post-legalization, respectively. Legalization was not associated with changes in any measured cannabis characteristics: cannabis use (OR 0.91, 95% CI 0.73-1.13), days of use/month (B -0.42, 95% CI - 2.05-1.21), money spent, or cannabis source. There was no association between legalization and prevalence of cannabis use on UDS (OR 1.67, 95% CI 0.93-2.99) or percentage of cannabis-positive UDSs (OR 1.00, 95% CI 0.99-1.01). Participants overwhelmingly reported that legalization would have no impact on their cannabis use (85.7%). CONCLUSIONS: Amongst patients treated for OUD, no significant change in cannabis use was observed following legalization; however, high rates of cannabis use are noted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".