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Open access
volume 24 issue 1 publication number 367

Virtual reality simulation for high-risk neonatal emergency nursing training: a mixed-methods study on nurse competency and outcomes

Publication typeJournal Article
Publication date2025-04-03
scimago Q1
wos Q1
SJR1.272
CiteScore4.5
Impact factor3.9
ISSN14726955
Abstract
Nurses in neonatal intensive care units face critical challenges in managing emergencies where timely, accurate interventions are essential for survival. Traditional nursing education often lacks the hands-on, immersive training required to build complex emergency skills, contributing to persistent neonatal mortality globally. Virtual reality (VR) simulation, grounded in Kolb’s Experiential Learning Theory, offers a promising solution by replicating realistic and repeatable clinical scenarios. While VR has shown potential in nursing education, its specific impact on high-risk neonatal emergencies remains underexplored. To evaluate the effectiveness of a VR simulation program in enhancing nurse competency and improving neonatal outcomes during emergency care, grounded in Kolb’s Experiential Learning Theory. A concurrent triangulation mixed-methods design was implemented over two weeks across four pediatric hospitals. Through stratified random sampling, 128 NICU nurses were allocated to VR simulation (n = 64) or traditional training (n = 64) groups. Quantitative data were collected using validated instruments (OSCE: CVI = 0.92, MCQ: α = 0.86) measuring clinical skills, knowledge retention, and decision-making accuracy. Qualitative data were gathered through semi-structured interviews (n = 24) exploring experiential aspects. The VR group showed significant improvements in clinical skills (OSCE: +16.1 points, p < 0.001, d = 1.58), decision-making accuracy (+ 16.7%, p < 0.001), and reduced stabilization times (-6.2 min, p < 0.001). Patient safety events decreased by 52% (p < 0.001). Thematic analysis revealed enhanced professional competence (83%), reduced clinical anxiety (75%), and positive learning experiences (88%), despite minor technical challenges. VR simulation demonstrates superior effectiveness for neonatal emergency training, significantly improving both nurse competency and patient outcomes. While geographic specificity and brief follow-up duration limit generalizability, findings support VR’s potential for enhancing emergency preparedness. Future research should address longitudinal outcomes and implementation across diverse healthcare settings. Not applicable.
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Alruwaili A. N. et al. Virtual reality simulation for high-risk neonatal emergency nursing training: a mixed-methods study on nurse competency and outcomes // BMC Nursing. 2025. Vol. 24. No. 1. 367
GOST all authors (up to 50) Copy
Alruwaili A. N., Alshammari A. M., Alhaiti A., Elsharkawy N. B., Ali S. I., Elsayed Ramadan O. M. Virtual reality simulation for high-risk neonatal emergency nursing training: a mixed-methods study on nurse competency and outcomes // BMC Nursing. 2025. Vol. 24. No. 1. 367
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TY - JOUR
DO - 10.1186/s12912-025-03038-4
UR - https://bmcnurs.biomedcentral.com/articles/10.1186/s12912-025-03038-4
TI - Virtual reality simulation for high-risk neonatal emergency nursing training: a mixed-methods study on nurse competency and outcomes
T2 - BMC Nursing
AU - Alruwaili, Abeer Nuwayfi
AU - Alshammari, Afrah Madyan
AU - Alhaiti, Ali
AU - Elsharkawy, Nadia Bassuoni
AU - Ali, Sayed Ibrahim
AU - Elsayed Ramadan, Osama Mohamed
PY - 2025
DA - 2025/04/03
PB - Springer Nature
IS - 1
VL - 24
SN - 1472-6955
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2025_Alruwaili,
author = {Abeer Nuwayfi Alruwaili and Afrah Madyan Alshammari and Ali Alhaiti and Nadia Bassuoni Elsharkawy and Sayed Ibrahim Ali and Osama Mohamed Elsayed Ramadan},
title = {Virtual reality simulation for high-risk neonatal emergency nursing training: a mixed-methods study on nurse competency and outcomes},
journal = {BMC Nursing},
year = {2025},
volume = {24},
publisher = {Springer Nature},
month = {apr},
url = {https://bmcnurs.biomedcentral.com/articles/10.1186/s12912-025-03038-4},
number = {1},
pages = {367},
doi = {10.1186/s12912-025-03038-4}
}