Fit-for-purpose solutions beyond supervised injection offer the next stage of harm reduction for the US drug epidemic
Bibliographic record
Abstract
Overdose Prevention Centers (OPCs) provide critical harm reduction services for people who use drugs. For over twenty years, such centers, including supervised injection facilities, have proven to be successful tools for combating drug epidemics with well-demonstrated benefits of reducing overdose deaths and the transmission of infectious disease such as HIV in participating communities in Europe, Australia, and Canada. Although there are limited exceptions in the US, the controversial nature of OPCs has prevented adoption across the country, thereby contributing to large numbers of preventable deaths. Analysis of CDC overdose death data demonstrates that the drug types causing deaths are highly variable by state and that while opioids (primarily fentanyl) are the most important contributor, a significant portion of overdose deaths do not involve opioids and are likely to involve other modes of consumption in addition to injection. Considering this finding, arguments are made for policy and facility strategy changes that would lead to development of new fit-for-purpose OPCs that are best suited to specific regions and likely more acceptable to individuals within these communities. Tailoring OPCs could accelerate destigmatization and increase adoption of OPCs in urban and non-urban communities to effectively manage this nationwide epidemic.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".