Addition of Advanced Practice Registered Nurses to the Trauma Team: An Integrative Systematic Review of Literature
Bibliographic record
Abstract
The total cost of inpatient care from a traumatic mechanism of injury in the United States between 2001 and 2011 was $240.7 billion. Medical resident work hour reductions mandated in 2011 left a shortage of available in-hospital providers to care for trauma patients. This created gaps in continuity of care, which can lead to costly increased lengths of stay (LOS) and increased medical errors. Adding advanced practice nurses (APNs) specializing in acute or trauma care to the trauma team may help fill this shortage in trauma care providers. The purpose of this integrative systematic review of the literature was to determine whether adding APNs to the admitting trauma team would decrease LOS. A systematic review of primary research in CINAHL and PubMed databases was performed using the following terms: nurse practitioner, advanced practice nurse, trauma team, and length of stay. Included studies examined the effects of adding APNs to trauma teams, were written in English, and were published in 2007-2017. Six studies were included in the final sample, and all were completed at Level I trauma centers in the United States except one from Canada. Combined sample size was 25,083 admitted trauma patients. All 6 studies reported a decrease in LOS ranging from 0.8 to 2.54 days when APNs were added to the trauma team. More research is needed to identify the best utilization of an APN on a trauma team. It is recommended that all trauma centers add APNs to the trauma team to not only decrease admitted trauma patients' LOS but also provide continuity of care, decreasing costs, and minimizing errors.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".