“And this is the life jacket, the lifeline they’ve been wanting”: Participant perspectives on navigating challenges and successes of prescribed safer supply
Bibliographic record
Abstract
BACKGROUND: In 2021, 43% of drug toxicity deaths in Ontario were reported by public health units serving medium-sized urban and rural communities. Safer supply programs (SSPs) have been primarily established in large urban centres. Given this, the current study is based on an evaluation of a SSP based in a medium-sized urban centre with a large catchment area that includes rural and Indigenous communities. The aim of this research paper is to understand the challenges and successes of the nurse practitioner-led SSP from the perspective of program participants. METHODS: Interpretive description was used to understand the experiences of 14 participants accessing a SSP. Each participant was interviewed using a semi-structured approach, and 13 of the interviewees also completed surveys accessed through Qualtrics. An iterative process using NVivo software was used to code interviews, and a constant comparative data analysis approach was used to refine and categorize codes to themes. FINDINGS: Three overarching themes were the result of this analysis: feeling better, renewed hope, and safety. These three themes capture the experiences of participants in the SSP, including both the challenges and successes they faced. CONCLUSION: The findings and subsequent discussion focus on both the key best practices of the program, and areas for future development and improvement. Despite barriers to services, prescribed SSPs are improving the lives of people who use drugs, and the current outcomes align with reports and evaluations from other SSPs across Canada.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".