Characterizing motivations for cannabis use in a cohort of people who use illicit drugs: A latent class analysis
Bibliographic record
Abstract
BACKGROUND: Cannabis use is common among marginalized people who use illicit drugs (PWUD) but reasons for use remain poorly investigated. We sought to explore how different intentions for cannabis use relate to social, structural, and behavioural factors among PWUD in Vancouver, Canada. METHODS: We used data from cannabis-using participants in two community-recruited prospective cohort studies of PWUD. Using latent class analysis, we identified discrete cannabis-using groups based on self-reported intentions for use. Generalized estimating equations were used to examine correlates of class membership. RESULTS: Between June 2016 and December 2018, 2,686 observations from 897 participants cannabis-using PWUD were analyzed. Four latent classes of cannabis use emerged: Class 1 (31.6%), characterized by non-medical purposes; Class 2 (37.5%), characterized by non-pain therapeutic use (e.g., stress, nausea/loss of appetite, and insomnia); characterized by Class 3 (21.9%) predominantly pain relief; and Class 4 (9.0%), characterized by a wide range of therapeutic uses in addition to pain management, including insomnia, stress, nausea/loss of appetite, and harm reduction. Class-specific structural, substance-, and health-related differences were observed, including indicators of better physical and mental health among the "recreational" class, despite evidence of more structural vulnerabilities (e.g., homelessness, incarceration). CONCLUSIONS: Our findings demonstrate a wide spectrum of motivations for cannabis use among PWUD. We observed important health-related differences between latent classes, demonstrating possible unmet healthcare needs among PWUD reporting therapeutic cannabis use. These findings inform ongoing policy surrounding access to cannabis for harm reduction purposes and applications of medical cannabis 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".