A qualitative evaluation of a fentanyl patch safer supply program in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: The ongoing overdose crisis in Canada has prompted efforts to increase access to a "safer supply" of prescribed alternatives to the unregulated drug supply. While safer supply programs predominantly distribute hydromorphone tablets, the Safer Alternatives for Emergency Response (SAFER) program in Vancouver, Canada offers a range of prescribed alternatives, including fentanyl patches. However, little is known about the effectiveness of fentanyl patches as safer supply. Drawing on the perspectives and experiences of program participants, we sought to qualitatively evaluate the effectiveness of the SAFER fentanyl patch program in meeting its intended aims, including reducing risk of overdose by decreasing reliance on the unregulated drug supply. METHODS: As part of a larger mixed-methods evaluation of SAFER, semi-structured qualitative interviews were conducted with 17 fentanyl patch program participants between February 2022 and April 2023. Thematic analysis of interview data focused on program engagement, experiences, impacts, and challenges. RESULTS: The flexible program structure, including lack of need for daily dispensation, the extended missed dose protocol, and community pharmacy patch distribution fostered engagement and enhanced autonomy. Improved management of withdrawal symptoms and cravings due to steady transdermal dosing led to reduced unregulated drug use and overdose risk. Participants also experienced economic benefits and improvements in overall health and quality of life. However, skin irritation and patch adhesion issues were key barriers to program retention. CONCLUSION: Our findings demonstrate the value of including fentanyl patch safer supply in the substance use continuum of care and offer insights for innovations in delivery of this intervention.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".