Perspectives on Diversion of Medications From Safer Opioid Supply Programs
Bibliographic record
Abstract
Importance: Safer supply programs were implemented in Canada to provide pharmaceutical-grade alternatives to the toxic unregulated drug supply. While research shows clinical benefits and reduced overdose mortality among safer supply patients, medication diversion remains a concern. Objective: To examine provider (prescribing clinicians and allied health professionals) and patient perspectives on diversion of opioids prescribed in safer supply programs. Design, Setting, and Participants: In 2021, qualitative interviews and sociodemographic questionnaires were conducted with patients and providers across 4 safer supply programs in Ontario, Canada. Interviews with 21 providers (physicians, nurse practitioners, and allied health professionals) and 52 patients examined experiences implementing safer supply or receiving care. Initial data analysis was conducted from December 2021 to March 2022, and the subanalysis focused on diversion was conducted from December 2023 to March 2024. Exposures: Participation in safer supply program as a patient or provider. Main Outcomes and Measures: Data about diversion were coded, extracted, and thematically analyzed. Results: Of 52 patient participants, 29 (55.8%) were men and 23 (44.2%) were women; 1 was Black (1.9%), 9 (17.3%) were Indigenous, 1 was Latino (1.9%), and 41 (78.8%) were White; and the mean (SD) age was 46.5 (9.6) years. Of 21 provider participants, 6 (28.6%) were men, 13 (61.9%) were women, and 2 (9.5%) were nonbinary; and the mean (SD) age was 37.6 (7.6) years. Participants characterized diversion as a spectrum ranging from no diversion, to occasional medication sharing and loss, to selling all prescribed doses of safer supply (considered rare and easy to detect). Most patients reported they consumed all or most of their prescribed medications and rarely shared or sold their doses. However, providers and patient participants shared that people might share, trade, and/or sell some of their medications with other opioid-using people for multiple reasons. Most prominent reasons for diversion were (1) compassionate sharing with intimate partners and friends to manage withdrawal and overdose risk; (2) selling or trading medications to address their own unmet substance use needs (eg, high opioid tolerance); and (3) medication loss due to poverty, homelessness, and associated vulnerabilities to theft and coercion. Programs used nonpunitive urine drug screening practices and patient self-report to monitor medication use. When diversion was identified, providers described using nonjudgmental conversations to understand patients' needs and develop mitigation strategies that addressed underlying reasons for diversion, including changing doses and medications prescribed to better match patients' needs, enrolling eligible intimate partners, and developing safety plans to mitigate vulnerabilities to theft and coercion. Conclusions and Relevance: Diversion encompasses a wide spectrum of practices (selling, sharing, and loss of medications), and occurs for complex reasons that surveillance and punitive measures are unlikely to mitigate. Diversion may be best addressed by expanding medication options to better match patients' diverse substance use needs and high tolerance, alongside wraparound social supports.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".