Staff perspectives on the impacts of the COVID-19 pandemic on the provision of emergency department care for patients who use opioids
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic and Canada's drug poisoning crisis placed exceptional demands on emergency departments (ED). We aimed to explore the impact of these intersecting crises from the perspectives of ED staff to understand how EDs can improve care and protect the health and well-being of patients who use opioids, ED staff, and healthcare providers. METHODS: We conducted a focused ethnographic study involving 29 semi-structured interviews with ED staff who cared for patients who use opioids during the pandemic. Interviews explored ED staff perspectives on how the pandemic impacted care for patients who use opioids and how EDs can better serve this population. We conducted latent content analysis and main theme generation was informed by the socioecological model. RESULTS: Four main themes emerged. First, there was a change in patient behaviors, which impacted provider-patient relationships. Second, hospital pandemic policies and resource limitations created new barriers to care. Third, community service alterations, including the shift to virtual care and uncertain availability of services, further complicated patient care. Finally, participants highlighted opportunities to strengthen systems of care, including enhanced hospital addiction resources, improved addiction care training, expanded harm reduction services, and more robust community services. CONCLUSIONS: The COVID-19 pandemic highlighted significant changes in ED care delivery for patients who use opioids. Efforts to enhance EDs should include anticipating the needs of people who use substances and the healthcare providers who care for them to mitigate unintended harm and ensure a more resilient healthcare system.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".