Frequent Cannabis Use Is Negatively Associated with Frequency of Injection Drug Use Among People Who Inject Drugs in a Canadian Setting
Bibliographic record
Abstract
Objectives: High levels of morbidity and mortality associated with injection drug use continue to represent a significant public health challenge in many settings worldwide. Previous studies have shown an association between cannabis use and decreased risk of some drug-related harms. We sought to evaluate the association between high-intensity cannabis use and the frequency of injection drug use among people who inject drugs (PWID). Methods: The data for this analysis were collected from three prospective cohorts of PWID in Vancouver, Canada, between September 2005 and May 2018. Generalized linear mixed-effects models were used to analyze the association between daily cannabis use and the frequency of injecting illegal drugs (i.e., self-reported average number of injections per month). Results: Among the 2,619 active PWID, the frequency of injection drug use was significantly lower among people who use cannabis daily compared with people who use it less than daily (adjusted odds ratio [AOR]=0.84, 95% confidence interval [CI]: 0.73–0.95). Sub-analyses indicated that this effect was restricted to the frequency of illegal opioid injection (AOR=0.78, 95% CI: 0.68–0.90); the association between daily cannabis use and the frequency of illegal stimulant injection was not significant (AOR=1.08, 95% CI 0.93–1.25). Discussion: The findings from these prospective cohorts suggest that people who use cannabis daily were less likely to report daily injection of illegal drugs compared with people who use it less than daily. These results suggest the potential value of conducting experimental research to test whether controlled administration of cannabinoids impacts the frequency of illegal opioid injection among PWID.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".