Prescribed safer supply during dual public health emergencies: a qualitative study examining service providers perspectives on early implementation
Bibliographic record
Abstract
BACKGROUND: Within North America and worldwide, drug related overdoses have increased dramatically over the past decade. COVID-19 escalated the need for a safer supply to replace unregulated substances and to reduce toxicity and overdoses. Service providers play an integral role in the delivery of safer supply but there is little empirical evidence that conceptualizes effective safer supply from their perspectives. This study explored early implementation and impacts of a safer supply program, capturing the perspectives of an interdisciplinary team of service providers on tensions and issues encountered in the development of the SAFER program. METHODS: Using a community-based participatory approach, we conducted individual interviews with program providers (n = 9). The research team was composed of researchers from a local drug user organization, a local harm reduction organization, and academic researchers. The Consolidated Framework for Implementation Research (CFIR) informed the interview guide. Data was analyzed using thematic analysis. RESULTS: There are six themes describing early implementation: (1) risk mitigation prescribing as context for early implementation; (2) developing SAFER specific clinical protocols; (3) accessibility challenges and program innovations; (4) interdisciplinary team and wraparound care; (5) program tensions between addiction medicine and harm reduction; (6) the successes of safer supply and future visions. CONCLUSION: Early implementation issues and tensions included prescriber concerns about safer supply prescribing in a highly politicized environment, accessibility challenges for service users such as stigma, encampment displacement, OAT requirements, program capacity and costs, and tensions between addiction medicine and harm reduction. Navigating these tensions included development of clinical protocols, innovations to reduce accessibility challenges such as outreach, wraparound care, program coverage of medication costs and prescribing safer supply with/without OAT. These findings contribute important insights for the development of prescribed 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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".