Bibliographic record
Abstract
BACKGROUND: As the overdose crisis in Canada continues to escalate in severity, novel interventions and programs are required. Safer Supply programs offer pharmaceutical-grade medication to people who use drugs to replace and decrease harms related to the toxic illicit drug supply. Given the paucity of research surrounding these programs, we sought to better understand the experience of being part of a Safer Supply program from the perspective of current participants. METHODS: We completed semi-structured interviews and surveys with Safer Supply participants in Ottawa, Canada. Interviews were audio-recorded, transcribed, and analyzed thematically. Descriptive statistics were used to report survey data. RESULTS: Participants most commonly discussed Safer Supply benefits. This included programs offering a sense of community, connection, hope for the future, and increased autonomy. Participants also described program concerns, such as restrictive protocols, inadequate drugs, and diversion. CONCLUSIONS: Our research demonstrated that participants found Safer Supply to be effective and impactful for their substance use goals. While participants did discuss concerns about the program, overall, we found that this is an important harm reduction-based program for people who use drugs in the midst of the overdose crisis.
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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 | 0.060 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".