‘I don't chase drugs as much anymore, and I'm not dead’: Client reported outcomes associated with safer opioid supply programs in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: The ongoing opioid overdose crisis, which has killed over 30,000 people in Canada since 2016, is driven by the volatility of an unregulated opioid drug supply comprised primarily of fentanyl. The Canadian government has recently funded safer opioid supply (SOS) programs, which include off-label prescriptions of pharmaceutical-grade opioids to high risk individuals with the goal of reducing overdose deaths. METHODS: In 2021, we examined the implementation and adaption of four SOS programs in Ontario. These programs use a primary care model and serve communities experiencing marginalisation. We conducted semi-structured interviews with program clients. We present the results of a thematic analysis with the aim of describing clients' self-reported impact of these programs on their health and well-being. RESULTS: We interviewed 52 clients between June and October 2021 (mean age 47 years, 56% men, 17% self-identified Indigenous, 14% living with HIV). Our results indicate multifaceted pathways to improved self-reported health and well-being among clients including changes to drug use practices, fewer overdoses, reduced criminalised activity, improved trust and engagement in health care, and increased social stability (e.g., housing). DISCUSSION AND CONCLUSION: Most clients reported that the intervention saved their life because of the reduced frequency of overdoses. Findings suggest that SOS programs improved clients' health outcomes and increase opportunities for engagement in health services. Our results provide insight into the mechanisms behind some of the emergent evidence on the impact of safer supply prescribing.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".