How an emergency department is organized to provide opioid-specific harm reduction and facilitators and barriers to harm reduction implementation: a systems perspective
Bibliographic record
Abstract
BACKGROUND: The intersection of dual public health emergencies-the COVID-19 pandemic and the drug toxicity crisis-has led to an urgent need for acute care based harm reduction for unregulated opioid use. Emergency Departments (EDs) as Complex Adaptive Systems (CASs) with multiple, interdependent, and interacting elements are suited to deliver such interventions. This paper examines how the ED is organized to provide harm reduction and identifies facilitators and barriers to implementation in light of interactions between system elements. METHODS: Using a case study design, we conducted interviews with Emergency Physicians (n = 5), Emergency Nurses (n = 10), and clinical leaders (n = 5). Nine organizational policy documents were also collected. Interview data were analysed using a Reflexive Thematic Analysis approach. Policy documents were analysed using a predetermined coding structure pertaining to staffing roles and responsibilities and the interrelationships therein for the delivery of opioid-specific harm reduction in the ED. The theory of CAS informed data analysis. RESULTS: An array of system agents, including substance use specialist providers and non-specialist providers, interacted in ways that enable the provision of harm reduction interventions in the ED, including opioid agonist treatment, supervised consumption, and withdrawal management. However, limited access to specialist providers, when coupled with specialist control, non-specialist reliance, and concerns related to safety, created tensions in the system that hinder harm reduction provision with resulting implications for the delivery of care. CONCLUSIONS: To advance harm reduction implementation, there is a need for substance use specialist services that are congruent with the 24 h a day service delivery model of the ED, and for organizational policies that are attentive to discourses of specialized practice, hierarchical relations of power, and the dynamic regulatory landscape. Implementation efforts that take into consideration these perspectives have the potential to reduce harms experienced by people who use unregulated opioids, not only through overdose prevention and improving access to safer opioid alternatives, but also through supporting people to complete their unique care journeys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".