Safer supply programs: Discussions on medication diversion, sharing, and selling
Bibliographic record
Abstract
BACKGROUND: Nearly 50,000 people who use drugs have died as a result of the ongoing drug poisoning crisis in Canada. To directly address concerns surrounding this crisis, safer supply pilot programs were implemented in several communities across the country. Since program implementation, discussions surrounding medication diversion have proliferated. We conducted surveys and interviews with current program participants to better understand medication diversion within the context of safer supply programs. METHODS: Safer supply program participants were recruited in Ottawa, Canada to complete semi-structured interviews and surveys. Surveys collected socio-demographic and substance use data. Survey results were reported using descriptive statistics. Semi-structured interviews were audio-recorded, transcribed, and analyzed thematically. RESULTS: 30 people participated in this study. From interviews, seven themes arose on the topic of diversion, including 1) diversion in the context of being a person who uses drugs, 2) safety, 3) compassion, 4) meeting needs, 5) survival, 6) coercion, and 7) protecting youth. CONCLUSION: Discussions with participants highlighted the importance of understanding why medication diversion occurs. Important factors influencing medication diversion included the need for safety, compassion, meeting needs, survival, and coercion faced by people who use drugs. Ultimately, medication diversion can be best understood as a measure implemented by people who use drugs to protect and care for their underserved community.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".