Motivations for and perspectives of medication diversion among clients of a safer opioid supply program in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: Safer opioid supply programs in Canada have come under intense scrutiny related to the perceived risk of diversion of safer opioid supply medications. We sought to explore the experiences and perspectives of safer opioid supply medication diversion with clients of a safer opioid supply program in Toronto, Canada. METHODS: From December 2022 to August 2023, we conducted in-depth, semi-structured interviews with 25 adult clients of a safer opioid supply program in Toronto, Canada. We analyzed the data using deductive and inductive approaches via thematic analysis. RESULTS: Our analysis identified five themes regarding clients' perceptions and experiences with safer opioid supply diversion: (i) Compassionate sharing with others to address withdrawal symptoms; (ii) Selling or sharing due to unmet medication or survival needs of program clients; (iii) High demand for safer alternatives to those that are available in unregulated drug markets; (iv) Price of safer opioid supply medications in the unregulated drug markets as a diversion deterrent; and (v) Coerced diversion through harassment or violence. CONCLUSIONS: These findings document experiences of medication diversion and the multifaceted and complex interplay of various individual and contextual factors that motivate safer opioid supply clients to engage in it. Future policy and safer opioid supply practice should address root causes of diversion, particularly barriers to service access and the diverse medication needs of clients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".