Safer opioid supply via a biometric dispensing machine: a qualitative study of barriers, facilitators and associated outcomes
Bibliographic record
Abstract
BACKGROUND: The MySafe program provides pharmaceutical-grade opioids to participants with opioid use disorder via a biometric dispensing machine. The objectives of this study were to examine facilitators and barriers to safer supply via the MySafe program and the associated outcomes. METHODS: We conducted semistructured interviews with participants who had been enrolled in the MySafe program for at least a month at 1 of 3 sites in Vancouver. We developed the interview guide in consultation with a community advisory board. Interviews focused on context of substance use and overdose risk, enrolment motivations, program access and functionality, and outcomes. We integrated case study and grounded theory methodologies, and used both conventional and directed content analyses to guide inductive and deductive coding processes. RESULTS: We interviewed 46 participants. Characteristics that facilitated use of the program included accessibility and choice, a lack of consequences for missing doses, nonwitnessed dosing, judgment-free services and an ability to accumulate doses. Barriers included technological issues with the dispensing machine, dosing challenges and prescriptions being tied to individual machines. Participant-reported outcomes included reduced use of illicit drugs, decreased overdose risk, positive financial impacts and improvements in health and well-being. INTERPRETATION: Participants perceived that the MySafe program reduced drug-related harms and promoted positive outcomes. This service delivery model may be able to circumvent barriers that exist at other safer opioid supply programs and may enable access to safer supply in settings where programs may otherwise be limited.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".