Preferred pharmaceutical-grade opioids to reduce the use of unregulated opioids: A cross-sectional analysis among people who use unregulated opioids in Vancouver, Canada
Bibliographic record
Abstract
OBJECTIVES: Many people who use drugs in the United States and Canada continue to access the contaminated unregulated drug supply, resulting in the ever-escalating overdose epidemic. In Canada, even in areas where healthcare providers are authorized to prescribe alternatives to the unregulated supply (e.g., prescribed safer supply), availability and accessibility are low. We sought to characterize the needs of people who use unregulated opioids in Vancouver, Canada by asking them whether access to any pharmaceutical opioids would reduce their use of unregulated opioids, and if so, which pharmaceutical opioids they preferred. METHODS: We analyzed data from participants who self-reported using unregulated opioids in three Vancouver-based prospective cohort studies between 2021 and 2022. We employed multivariable logistic regression to identify factors associated with reporting a preferred pharmaceutical opioid to reduce unregulated opioid use. RESULTS: Of 681 eligible participants, 504 (74.0 %) identified a preferred pharmaceutical opioid to reduce unregulated opioid use. The most commonly reported preferred opioids included: diacetylmorphine (42.9 %), fentanyl patches (11.1 %), and fentanyl powder (10.5 %). Overall, 5.6 % of participants who identified diacetylmorphine, 12.5 % of participants who identified fentanyl patches, and no participants who identified fentanyl powder as their preferred opioids reported receiving prescriptions of them. In multivariable analysis, exposure to benzodiazepines through unregulated drug use (adjusted odds ratio [AOR] = 2.57; 95 % confidence interval [CI] = 1.69-3.90), and receipt of prescribed safer supply of opioids without opioid agonist therapy (OAT; AOR = 2.66; 95 % CI = 1.12-6.36) within the past six months were significantly associated with reporting a preferred pharmaceutical opioid. CONCLUSION: Three-quarters of participants reported that receiving prescribed pharmaceutical opioids of their preference could reduce their use of unregulated opioids; however, the proportions of those actually being prescribed their preferred opioids were very low. Further, these participants were also more likely to report exposure to benzodiazepine-adulterated drugs. Our findings provide important implications for future safer supply programs.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".