Challenges of implementing safer supply programs in Canada during the COVID-19 pandemic: A qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Canada is experiencing an unprecedented drug toxicity crisis driven by a highly toxic unregulated drug supply contaminated with fentanyl, benzodiazepine, and other drugs. Safer supply pilot programs provide prescribed doses of pharmaceutical alternatives to individuals accessing the unregulated drug supply and have been implemented to prevent overdose and reduce related harms. Given the recent emergence of these pilot programs and the paucity of data on implementation challenges, we sought to document challenges in their initial implementation phase. METHODS: We obtained organizational progress reports from Health Canada, submitted between 2020 and 2022 by 11 pilot programs located in British Columbia, Ontario, and New Brunswick. We analyzed the data using deductive and inductive approaches via thematic analysis. Analyses were informed by the consolidated framework for implementation research. RESULTS: We obtained 45 progress reports from 11 pilot programs. Six centres were based in British Columbia, four in Ontario, and one in New Brunswick. Four overarching themes were identified regarding the challenges faced during the establishment and implementation of pilot programs: i) Organizational features (e.g., physical space constraints, staff shortages); ii) Outer contexts (e.g., limited operational funds and resources, structural inequities to access, public perceptions); iii) Intervention characteristics (e.g., clients' unmet medication needs); and iv) Implementation process (e.g., pandemic-related challenges, overly medicalized and high-barrier safer supply models). CONCLUSIONS: Safer supply pilot programs in Canada face multiple inner and outer implementation challenges. Given the potential role of safer supply programs in addressing the drug toxicity crisis in Canada and the possibility of future scale-up, services should be well-supported during their implementation phases. Refining service provision within safer supply programs based on the feedback and experiences of clients and program administrators is warranted, along with efforts to ensure that appropriate medications are available to meet the clients' needs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".