An Examination of How People Who Use Drugs Conceptualize the Benefits and Drawbacks of Using Overdose Prevention Centers
Bibliographic record
Abstract
Background: In 2021, NYC implemented overdose prevention center (OPC) services at two existing syringe exchange programs, allowing people to use pre-obtained drugs on-site. Although OPCs in Canada, Western Europe, and Australia have demonstrated their feasibility and benefits towards reducing overdose risk and drug-related harm, there is less data on how people who use drugs (PWUD) conceptualize the benefits and any potential drawbacks of using OPCs. Methods: In June-August 2022, we conducted 26 semistructured interviews with people in New York City who used unprescribed opioids. Interviews lasted 30-60 min and were conducted remotely using Zoom and later transcribed by a professional service. Data were then coded, using AtlasTi, into meaningful categories using a thematic approach based on the aims of the study and existing literature. Results: Most participants had heard of OnPoint and reported a willingness to use it. They described the ability of OnPoint staff to reverse an overdose quickly and the presence of naloxone, oxygen, and other supplies as the primary benefits. Yet, many also noted that OPCs provide PWUD with a place to escape from the weather and/or avoid law enforcement. Participants also reported concerns about how far PWUD would be willing to travel or wait to use an OPC and for the autonomy of PWUD in the context of formal, sanctioned OPCs. Conclusions: Results suggest that many PWUD in NYC are well-informed about OnPoint and are willing to use OPCs. Yet, to fulfil their potential, OPCs must be located near to where PWUD live, and should be made as low-threshold as possible. However, since it is unlikely that OPCs will be expanded enough to meet the need and because some PWUD will never choose to use in sanctioned OPCs, expanding the reach of alternative strategies, such as Mobile Overdose Response Services, is recommended.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".