Perspectives on prescribed safer supply programs among people who inject drugs in Baltimore, Maryland: A mixed methods study among individuals highly affected by drug toxicity
Bibliographic record
Abstract
• U.S. study finds 87 % of PWID interested in prescribed safer supply. • Mixed methods with respondent-driven sampling in Baltimore (May-June 2024). • Older and unhoused participants showed highest interest in safer supply. • Implementation preferences strongly linked to program acceptability. • Key benefits: overdose safety, reduced crime, improved life stability. To explore interest in, perspectives on, and implementation considerations for prescribed safer supply programs among people who inject drugs in the U.S. Rapid mixed methods assessment May – June 2024 in Baltimore, Maryland, using respondent driven and criterion sampling among people reporting past year drug injection. Interviewer-administered surveys ( n = 300) and explanatory embedded in-depth interviews ( n = 26). Analysis of prescribed safer supply interest and implementation considerations included descriptive quantitative analysis, thematic qualitative analysis, and mixed methods joint display. Using estimated population-level data, 87% (95% C.I. 77.0 - 95.9) were interested in participating in prescribed safer supply if available and legal. Interest was highest among older and unhoused participants and strongly associated with specific implementation preferences. Qualitative data revealed three explanations: Safety from overdose and other hazards of unpredictable street drugs; Reduced drug market crime and violence; and Life stability due to managed use and regular services. This study suggests strong acceptability of prescribed safer supply to address the U.S. opioid crisis, including overdose, violence, and associated healthcare costs. We recommend further U.S. research and pilot projects.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".