Emergency physician perspectives on initiating buprenorphine/naloxone in the emergency department: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to examine the perspectives of Canadian emergency physicians on the care of patients with opioid use disorders in the emergency department (ED), in particular the real-world facilitators to prescribing buprenorphine/naloxone (BUP) in the ED. METHODS: We conducted semistructured qualitative interviews using a multi-site-focused ethnographic design. Purposive sampling via an existing national research network was used to recruit ED physicians. Interviews were conducted by phone using an interview guide and continued until theoretical data saturation was reached. Interviews were transcribed and analyzed using latent content analysis. Interviews took place between June 21, 2019, and February 11, 2020. RESULTS: A total of 32 physicians were included in the analysis. Participants had a median of 10 years of experience, and most (29/32) worked in urban settings. Clinical care of patients with opioid use disorder was found to be variable and physician dependent. Although some physicians reported routinely prescribing BUP, others felt that this was outside the clinical scope of emergency medicine. Access to clinical pathways, incentivized training, dedicated human resources, and follow-up care were identified as critical facilitators for supporting BUP prescribing. Participants also identified a shared responsibility between patients and the ED, including the importance of a patient-centered approach that enhanced patient autonomy. ED BUP prescribing became self-reinforcing over time. CONCLUSIONS: Although there remains practice variability among Canadian emergency physicians, successful implementation of ED BUP prescribing has occurred in some locations. Jurisdictions wanting to facilitate BUP uptake should consider providing incentivized training, treatment protocols, dedicated human resources, and streamlined access to follow-up care.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".