Perspectives of people who use drugs on implementing overdose response technologies in acute care settings: a qualitative study
Bibliographic record
Abstract
BACKGROUND: People who use drugs (PWUD) face many barriers in healthcare settings. Illicit substance use during hospital stays, including solitary use in bathrooms, is prevalent, leading to a higher risk of overdoses. In this study, we examine the views of PWUD on implementing novel strategies such as overdose response technologies (ORTs) into acute care and explore the perceived acceptability, impacts, and barriers of these interventions. METHODS: We used convenience sampling to recruit 10 participants from hospitals and addiction medicine clinics, and semi-structured interviews were conducted. The interviews included an explanation of the five main types of ORTs relevant to acute care settings (hotlines, applications, overdose buttons, reverse motion detectors, and wearables), followed by questions where the participant had to critically evaluate whether each ORTs would be effective for each scenario. Open-ended coding and thematic analysis were used, and themes were derived from the data as it was reviewed. RESULTS: Participants acknowledged the advantages and potential risks of integrating ORTs into acute care. It was recognized that ORTs could help improve the relationships between PWUD and healthcare providers, reduce mortality rates in hospital bathrooms, and provide peer support during hospital stays. PWUD highlighted privacy concerns, logistical barriers, and stated that ORTs can also negatively impact their relationships with healthcare providers due to stigma. CONCLUSION: Although many participants felt that incorporating ORTs would be an advantage to their care within hospitals, our study also highlighted implementation barriers and broader policy changes that need to be addressed. Working towards addressing such barriers and changes can allow ORTs to be the next tool to help mitigate barriers faced by PWUD.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".