Use of Cannabis as a Harm Reduction Strategy Among People Who Use Drugs: A Cohort Study
Bibliographic record
Abstract
Introduction: While substance use contributes to a substantial burden of disease, access to evidence-based harm reduction interventions remains limited or inaccessible. Preliminary research suggests that some individuals use cannabis to reduce the harms associated with their use of other substances, including opioids and stimulants. This study examines factors associated with the self-reported use of cannabis for harm reduction among people who use drugs (PWUD). Methods: We drew data from three prospective, community-recruited cohorts of PWUD in Vancouver, Canada, between June 2016 and May 2018. Multivariable generalized linear mixed-effects modeling was used to examine factors associated with the primary outcome of “use of cannabis for harm reduction,” defined as self-reported use of cannabis to substitute for other substances, treat withdrawal, or come down off other drugs. Results: One thousand nine hundred thirty-six participants contributed 5706 observations. In adjusted analyses, daily methamphetamine use (adjusted odds ratio [AOR]=1.43, 95% confidence interval [CI]: 1.09–1.89), experiencing barriers to accessing addiction treatment (AOR=1.92, 95% CI: 1.21–3.03), and enrollment in addiction treatment modalities other than opioid agonist therapy (AOR=1.64, 95% CI: 1.17–2.29) were positively associated with using cannabis for harm reduction. Older age was negatively associated (AOR=0.97, 95% CI: 0.95–0.98). Among 1281 (66.2%) participants who use cannabis, daily cannabis use and obtaining cannabis from unregulated dispensaries were also independent correlates of using cannabis for harm reduction. Discussion and Conclusions: Individuals who were more likely to use cannabis for harm reduction reported difficulty accessing addiction treatment or used substances, such as methamphetamines, where effective treatments are limited. These findings highlight the need to better understand the potential harm-reducing impacts of cannabis among PWUD in these scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.003 |
| 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 teacher head, 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".