Understanding Steps and Challenges to Take-home Naloxone and Buprenorphine/naloxone Implementation in Québec Emergency Rooms: Suboxed Project
Bibliographic record
Abstract
Abstract Background Deaths attributable to drug abuse are on the rise across Canada. It is estimated that there were more than 13,900 opioid-related deaths from January 2016 to June 2019 in the country. Emergency departments (EDs) are often on the frontline of care provided to people at risk of opioid overdose within Québec’s healthcare system. A variety of programs to implement take-home naloxone distribution and/or the provision of opioid agonist treatment for ED patients who are at risk for overdose have been created in the United States and in Europe. However, few EDs in Canada have developed protocols for the provision of take-home naloxone and/or opioid agonist treatment by ED doctors. Methods A clinical algorithm for take home naloxone (THN) and prescription of buprenorphine/naloxone (B/N) was implemented in three EDs of Québec, Canada. This first phase of the SuboxED project required selecting clinical experts, describing the patient population, and creating partnerships with pharmacists and opioid agonist treatment clinics. Results: The clinical experts developed tools based on literature reviews and national and international guidelines. They also created educational tools and trained over 328 ED clinical staff. In addition, SuboxED ensured that a supply of take-home naloxone and B/n was available in the three ED sites for the study. Conclusion Implementing the proposed clinical algorithm for THN and prescription of B/N was challenging: drug supply and ED staff’s buy-in were among the most notable difficulties of SuboxED. Planning training sessions at three different institutions, each with its own governance structure and clinical culture, local realities and harm reduction priorities was complicated. Engaging already overworked ED teams consistently working in a gridlocked environments, revealed in itself to be a difficult endeavour.In the next phase of SuboxED, we will focus on data collection and analysis to evaluate both the implementation of the protocol through a retrospective review of electronic health records and satisfaction surveys of patients and healthcare professionals. Trial registration : none
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".