“You don’t have to squirrel away in a staircase”: Patient motivations for attending a novel supervised drug consumption service in acute care
Bibliographic record
Abstract
BACKGROUND: Acute care hospitals have been described as a high risk environment for people who use drugs (PWUD). Formal and informal bans on drug use can lead patients to conceal their use and consume under unsafe circumstances. Provision of hospital-based supervised consumption services (SCS) could help reduce drug-related harms and improve patient care. However, no peer-reviewed research documents patient experiences with attending SCS in this setting. To address this gap, the present study examines key factors that shape patients' decisions to attend or not attend a novel SCS embedded within a large, urban acute care hospital in Western Canada. METHODS: We adopted a focused ethnographic design and conducted 28 semi-structured interviews with SCS-eligible patients. We examined participant accounts thematically, and Rhodes' "Risk Environment" framework helped guide our analysis. RESULTS: Most participants perceived the SCS as a safer environment that made it possible to reduce drug-related risks and avoid using in unsafe areas of the hospital where they could be caught by staff, security, or police. However, some participants did not trust that the SCS would provide adequate protection from criminalization, which motivated them to avoid the site. Several participants also worried about the potential for unwanted changes to their patient care following SCS use. Physical site and policy limitations, such as eligibility requirements and a lack of infrastructure to support supervised inhalation, were additional reasons for not attending the SCS. CONCLUSION: PWUD in this study attended the hospital-based SCS in an attempt to reduce risks associated with their hospital stay. However, we note a number of access barriers that should be addressed to ensure optimal uptake. Wider provision of SCS in acute care requires both changes to the hospital environment and broader drug policy reform.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".