Use of Cannabis for Harm Reduction Among People at High Risk for Overdose in Vancouver, Canada (2016–2018)
Bibliographic record
Abstract
Objectives. To characterize the prevalence and reasons for the use of cannabis as a strategy to reduce the harms arising from other substances. Methods. We drew data about recent cannabis use and intentions from 3 prospective cohort studies of marginalized people who use drugs based in Vancouver, Canada, from June 2016 to May 2018. The primary outcome was “use of cannabis for harm reduction,” defined as using cannabis for substitution for licit or illicit substances such as heroin or other opioids, cocaine, methamphetamine, or alcohol; treating withdrawal; or coming down off other drugs. Results. Approximately 1 in 4 participants reported using cannabis for harm reduction at least once during the study period. The most frequent reasons included substituting for stimulants (50%) and substituting for illicit opioids (31%). Conclusions. The use of cannabis for harm reduction is a common strategy among people who use drugs in our setting. Further research into the factors associated with this strategy is needed. Better characterization of the risks and benefits of substitution strategies, including for opioids and stimulants, may prompt new treatment options for PWUD.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".