Development of pediatric simulation-based education - a systematic review.

BACKGROUND: This systematic literature review explored the general characteristics, validation, and reliability of pediatric simulation-based education (P-SBE). METHODS: A literature search was conducted between May 23 and 28 using the PRISMA guidelines, which covered databases such as MEDLINE, EMBASE, CINAHL, and Cochrane Library. In the third selection process, the original texts of 142 studies were selected, and 98 documents were included in the final content analysis. RESULTS: A total of 109 papers have been published in the ten years since 2011. Most of the study designs were experimental studies, including RCT with 76 articles. Among the typologies of simulation, advanced patient simulation was the most common (92), and high-fidelity simulation was the second most common (75). There were 29 compatibility levels and professional levels, with 59 scenarios related to emergency interventions and 19 scenarios related to communication feasibility and decision making. Regarding the effect variable, 65 studies confirmed that skills were the most common. However, validity of the scenarios and effect variables was not verified in 56.1% and 67.3% of studies, respectively. CONCLUSION: Based on these findings, simulation based-education (SBE) is an effective educational method that can improve the proficiency and competence of medical professionals dealing with child. Learning through simulation provides an immersive environment in which learners interact with the presented patient scenario and make decisions, actively learning the attitudes, knowledge, and skills necessary for medical providers. In the future, it is expected that such research on SBE will be actively followed up and verified for its validity and reliability.

Development of a Nurse Turnover Prediction Model in Korea Using Machine Learning.

Nurse turnover is a critical issue in Korea, as it affects the quality of patient care and increases the financial burden on healthcare systems. To address this problem, this study aimed to develop and evaluate a machine learning-based prediction model for nurse turnover in Korea and analyze factors influencing nurse turnover. The study was conducted in two phases: building the prediction model and evaluating its performance. Three models, namely, decision tree, logistic regression, and random forest were evaluated and compared to build the nurse turnover prediction model. The importance of turnover decision factors was also analyzed. The random forest model showed the highest accuracy of 0.97. The accuracy of turnover prediction within one year was improved to 98.9% with the optimized random forest. Salary was the most important decision factor for nurse turnover. The nurse turnover prediction model developed in this study can efficiently predict nurse turnover in Korea with minimal personnel and cost through machine learning. The model can effectively manage nurse turnover in a cost-effective manner if utilized in hospitals or nursing units.