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.

Arrayed MoS2 -In0.53 Ga0.47 As van der Waals Heterostructure for High-Speed and Broadband Detection from Visible to Shortwave-Infrared Light.

A high-speed and broadband 5 × 5 photodetector array based on MoS2 /In0.53 Ga0.47 As heterojunction is successfully demonstrated to take full advantage of the type-II band-aligned multilayer MoS2 /In0.53 Ga0.47 As. The fabricated devices exhibit good uniformity in the Raman spectrum and clear rectifying characteristics. The fabricated MoS2 /In0.53 Ga0.47 As photodetectors show good optical performances at a broad wavelength range showing high responsivities corresponding to the detectivity of ≈1010 Jones at -3 V for the incident broadband light from 400 to 1550 nm. A very fast photoresponse is also obtained with a small rise/fall time in the order of microseconds both for visible (638 nm) and shortwave infrared (1310 nm). Finally, the image scanning properties of MoS2 /In0.53 Ga0.47 As devices are demonstrated for visible and infrared light, indicating that the suggested device is one of the promising options for future broadband imager, which can be integrated on the focal plane arrays (FPAs).

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.

Nursing Students' Subjective Happiness: A Social Network Analysis.

Improving nursing students' subjective happiness is germane for efficiency in the nursing profession. This study examined the subjective happiness of nursing students by applying social network analysis (SNA) and developing a strategy to improve the subjective happiness of nursing. The study adopted a cross sectional survey to measure subjective happiness and social network of 222 nursing students. The results revealed that the centralization index, which is a measure of intragroup interactions from the perspective of an entire network, was higher in the senior year compared with the junior year. Additionally, the indegree, outdegree, and centrality of the social network of students with a high level of subjective happiness were all found to be high. This result suggests that subjective happiness is not just an individual's psychological perception, but can also be expressed more deeply depending on the subject's social relationships. Based on the study's results, to strengthen self-efficacy and resilience, it is necessary to utilize strategies that activate group dynamics, such as team activities, to improve subjective happiness. The findings can serve as basic data for future research focused on improving nursing students' subjective happiness by consolidating team-learning social networks through a standardized program approach within a curriculum or extracurricular programs.