Smoking identified as preferred mode of opioid safe supply use; investigating correlates of smoking preference through a 2021 cross-sectional study in British Columbia
Bibliographic record
Abstract
BACKGROUND: The increasing number of illicit drug toxicity deaths in British Columbia (BC) has led to calls for a regulated (pharmaceutical grade) supply of substances ("safe supply"). In order to inform safe supply recommendations, we aimed to identify why people currently smoke opioids and assess the preferred mode of consumption if people who use opioids were provided with opioid safe supply. METHODS: The BC Harm Reduction Client Survey (HRCS) is an annual survey that gathers information about people who use drugs' (PWUD) substance use characteristic with the goal of contributing to evidence-based policy. This study utilized data from the 2021 HRCS. The outcome variable was "prefer smoking opioid safe supply" ('yes/no'). Explanatory variables included participants' demographics, drug use, and overdose characteristics. Bivariate and hierarchical multivariable logistic regressions were conducted to identify factors associated with the outcome. RESULTS: Of 282 total participants who indicated a preference for a mode of consumption for opioid safe supply, 62.4% preferred a smokable option and 19.9% preferred to inject if provided with opioid safe supply. Variables significantly associated with the outcome (preferred smoking) included: being 19-29 years old (AOR=5.95, CI =1.93 - 18.31) compared to >50 years old, having witnessed an overdose in the last 6 months (AOR=2.26, CI=1.20 - 4.28), having smoked opioids in the last 3 days (AOR=6.35, CI=2.98 - 13.53) and having a preference to smoke stimulants safe supply (AOR=5.04, CI=2.53 - 10.07). CONCLUSION: We found that over half of participants prefer smokable options when accessing opioid safe supply. Currently in BC, there are limited smokable opioid safe supply options as alternatives to the toxic street supply. To reduce overdose deaths, safe supply options should be expanded to accommodate PWUD that prefer smoking opioids.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".