Reasons for Enrolling in Safer Supply Programs: A Longitudinal Qualitative Study on Participant Goals and Related Outcomes in the MySafe Program
Bibliographic record
Abstract
OBJECTIVE: Safer supply programs are a novel response to the ongoing overdose crisis in Canada--providing people at high overdose risk with a safer alternative to the highly toxic unregulated drug supply. The MySafe program provides pharmaceutical-grade opioids to participants via biometric dispensing machines. This study examines program-related goals and related outcomes across time. METHOD: Longitudinal, semi-structured interviews were conducted with 29 study participants at baseline and 1-year follow-up. Interviews covered program functionality, experiences, outcomes, and reasons for enrollment and engagement. Baseline and follow-up interviews were compared to explore changes over time, including the effectiveness of the MySafe program in supporting individuals' achievement of their stated goals. RESULTS: Most participants reported similar goals at their baseline and follow-up interviews. The most common goal for initiating and staying in the program was to stop or reduce using street-purchased drugs, followed by abstinence and wanting to stop injecting drugs. Several participants described goals addressing issues related to structural vulnerability (e.g., improving living situations). At follow-up, some participants reported reducing street-purchased drug use, no participants reported abstinence, and all those wanting to stop injecting drugs reported achieving their goals. CONCLUSIONS: Our findings highlight a strong desire among study participants to be separated from the unpredictable street drug supply. Participants reported variable success in attaining their stated goals. However, our results demonstrate the need for such programs to better attend to participant goals, especially those affected by structural vulnerability, that can be supported with wrap-around social and health care supports.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".