Exploring the implementation of nurses’ advanced competencies in emergency departments: a scoping review
Bibliographic record
Abstract
Emergency department (ED) overcrowding is a critical issue that compromises patient safety, prolongs waiting times, and increases staff workload. Contributing factors include insufficient primary-community care integration, staffing shortages, operational inefficiencies, and an ageing population with complex chronic conditions. These pressures are further exacerbated during disasters and are expected to worsen with the rising frequency of climate-related crises. Task shifting and the expansion of advanced nursing roles have been proposed as strategies to mitigate overcrowding; however, their adoption remains limited. This scoping review aims to map the existing evidence on advanced nursing practice in EDs, describing roles, outcomes, facilitators, and barriers. Following Joanna Briggs Institute methodology and PRISMA-ScR guidelines, we searched PubMed, Embase, and Scopus, without date restrictions, for original studies from high-income countries in which nurses autonomously performed functions beyond standard care. Of 3,029 records, 105 met the inclusion criteria, with most studies originating from Canada, Australia, and the USA. Three role categories were identified: (1) autonomous management of specific presentations ("See and treat"); (2) nurse-led patient flow management; and (3) triage nurse ordering, which allows nurses to order investigations or initiate treatment for predefined conditions at triage. Across settings, these models demonstrated comparable quality of care, clinical effectiveness, and patient and staff satisfaction to physician-led management, while often reducing waiting times and healthcare costs. Despite evidence being heterogeneous and largely single center, the findings support the safety and effectiveness of advanced nursing roles in EDs. This review highlights current research gaps and provides a foundation for designing multicenter trials and pilot programs to optimize the integration of advanced nursing competencies into ED systems.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".