Impact of Nurse Practitioner Role in Emergency Departments
Bibliographic record
Abstract
Background Overcrowding and long wait times in the emergency department (ED) have resulted in decreased patient satisfaction and quality of care. One of the solutions proposed to address wait times is the introduction of the nurse practitioner (NP) role in the ED. We present a systematic mixed studies review protocol that aims to gather and analyze available knowledge on the impact of the NP role in the ED on patients, other healthcare providers, and organizations. Methods The review will employ a mixed studies analysis approach. Data will be gathered from peer-reviewed and grey literature in English with no time limit. All international publications on the impact of NP role implementation that meets the inclusion criteria in the ED setting will be included. Each study will be appraised for quality using the mixed methods appraisal tool and data extracted by two independent authors. In the presence of conflict, a third author will provide a resolution. Study characteristics and findings will be synthesized using descriptive analysis, meta-analysis, and a three-stage thematic analysis approach. The review results will be presented using the PRISMA checklist for systematic reviews. Conclusions The systematic review will present current evidence on the impact of NP role implementation in the ED setting. The results are anticipated to support decisions and policymakers in their quest to decrease ED wait times and improve the quality of patient care in healthcare settings. Keywords: Nursing, Nurse Practitioner, Emergency Department, Patient Care, Systematic Review
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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 | 0.029 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".