Implementation of a nurse-led overdose prevention site in a hospital setting: lessons learned from St. Paul’s Hospital, Vancouver, Canada
Bibliographic record
Abstract
OBJECTIVES: In May 2018, St. Paul's Hospital (SPH) in Vancouver (Canada) opened an outdoor peer-led overdose prevention site (OPS) operated in partnership with Vancouver Coastal Health and RainCity Housing. At the end of 2020, the partnered OPS moved to a new location, which created a gap in service for SPH inpatients and outpatients. To address this gap, which was magnified by the COVID-19 pandemic, SPH opened a nurse-led OPS in February 2021. This paper describes the steps leading to the implementation of the nurse-led OPS, its impact, and lessons learned. METHODS: Four steps paved the way for the opening of the OPS: (1) identifying the problem, (2) seeking ethics guidance, (3) adapting policies and practices, and (4) supporting and training staff. RESULTS: The OPS is open between 10:00 and 20:00 and staffed by two nurses per shift. It is accessible to all patients including inpatients, patients in the Emergency Department, and patients attending outpatient services. Between February 1, 2021 and October 23, 2021, the OPS recorded 1612 visits for the purpose of injection, for an average weekly visit number of 42. A total of 46 overdoses were recorded in that 9-month period. Thirty-seven (80%) required administration of naloxone and 12 (26%) required a code blue response. CONCLUSIONS: Due to the unique nature of our OPS, we learned many important lessons in the process leading to the opening of the site and the months that followed. We conclude the paper with lessons learned grouped into six main categories, namely engagement, communication, access, staff education and support, data collection, and safety.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".