Physician perspectives on reducing harm and supporting emergency department patients who use drugs
Bibliographic record
Abstract
BACKGROUND: People who use drugs (PWUD) frequently seek care in the emergency department (ED). Little is known about ED physician perspectives and experiences integrating a harm reduction approach into care, including interventions that reduce the health, social and legal consequences of drug use without requiring a reduction in drug use. OBJECTIVE: This study aimed to describe the experiences of Canadian emergency physicians caring for PWUD, and facilitators and barriers to implementing harm reduction interventions in the ED. METHODS: Purposive sampling, using an existing national network, and snowball sampling techniques were used to recruit practicing emergency physicians. Semi-structured, one-on-one telephone interviews were conducted until theoretical data saturation was achieved. Interview recordings were transcribed and analyzed using latent content analysis. Interviews took place between June 2019 and February 2020. This work is a secondary analysis specifically focused on harm reduction approaches to care. RESULTS: 32 physician interviews were included. Participants had a median of 10 years of experience (range 1-33) and most (29/32) worked in urban EDs. Participants highlighted the complexities of caring for PWUD, including the intersection of structural vulnerability with substance use. The ED environment varied across Canada and either facilitated or hindered the adoption of harm reduction interventions. Additional barriers included a lack of training and experience; lack of community follow-up care; insufficient ED funding and staffing resources; and, tensions over the appropriate scope of emergency medicine practice. Facilitators included tailored education and training; specialized multidisciplinary teams; ED harm reduction champions; and standardized protocols. CONCLUSIONS: Though variability existed in the adoption and practice of harm reduction in Canadian EDs, most interviewed physicians supported a harm reduction approach to care. To facilitate widespread ED adoption of harm reduction interventions, there is a need for standardized guidance, supplemental resources, facilitated culture change, and sufficient community-based services.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".