Implementing Safer Supply programs: A comparative case study
Bibliographic record
Abstract
Background Harms related to the drug poisoning crisis in Canada continue to worsen, with 20 people dying each day from opioid toxicity related to the illicit drug supply. Safer Supply pilot programs have been implemented in a number of communities as a response to this crisis. This study provides an overview of the planning and implementation of Safer Supply programs in Ottawa, Canada.Methods A comparative case study was undertaken to provide a detailed description of the three models of Safer Supply programs in Ottawa, Canada. Portions of the Exploration, Preparation, Implementation, Sustainment framework were used to outline the outer and inner context as well as innovation factors which led to the implementation of these programs.Results Three unique Safer Supply programs were implemented in Ottawa. Factors that supported this included the innovation (Safer Supply) being originally conceptualized by people who use drugs, the inclusion of wrap-around services for clients, and ongoing teamwork among the partner organizations.Conclusions While each participating organization operates under separate funding models and discrete domains of care, ultimately, we have demonstrated that Safer Supply can be implemented in many different contexts, providing the foundation for scaling this program in other communities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".