Safer Supply: Program Discontinuation and Re-engagement
Bibliographic record
Abstract
Since 2016, nearly 50,000 people who use drugs have died as a result of the toxic unregulated drug supply. Safer supply programs provide people who use drugs with daily access to pharmaceutical-grade prescription medication as an alternative to the toxic unregulated drug supply. We conducted a qualitative study with safer supply participants to better understand program discontinuation, re-engagement, and barriers to care from their perspective. Semi-structured interviews and surveys were completed with participants. Overall, 30 individuals participated in this study. Three major themes were brought up by research participants, which include: (1) safer supply program entry, (2) safer supply program experiences, and (3) the program restart process. Discussions with participants highlighted the importance of recognizing that times of crisis are inevitable and may potentially threaten participant program retention. Having clear program processes in place, increased wrap-around services, and flexibility when provisioning care are essential components of safer supply programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".