Impact of safer supply programs on injection practices: client and provider experiences in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Fentanyl has contributed to a sharp rise in the toxicity of the unregulated drug supply and fatal overdoses in Canada. It has also changed injection practices. Injection frequency has increased as a result and so has equipment sharing and health-related risks. The aim of this analysis was to explore the impact of safer supply programs on injection practices from the perspective of clients and providers in Ontario, Canada. METHODS: The data set included qualitative interviews with 52 clients and 21 providers that were conducted between February and October 2021 across four safer supply programs. Interview excerpts discussing injection practices were extracted, screened, coded and then grouped into themes. RESULTS: We identified three themes, each theme corresponding to a change in injection practices. The first change was a decrease in the amount of fentanyl used and a decrease in injection frequency. The second change involved switching to injecting hydromorphone tablets instead of fentanyl. Finally, the third change was stopping injecting altogether and taking safer supply medications orally. CONCLUSION: Safer supply programs can contribute to reducing injection-related health risks in addition to overdose risks. More specifically, they have the potential to address disease prevention and health promotion gaps that stand-alone downstream harm reduction interventions cannot address, by working upstream and providing a safer alternative to fentanyl.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".