Purpose
Simulation-based education plays a critical role in nursing by allowing students to acquire clinical competencies in a safe and controlled environment. However, current evaluation tools for simulation scenarios often lack standardization, resulting in inconsistencies when assessing the effectiveness of such programs.
Methods
This study aimed to develop a comprehensive Nursing Simulation Scenario Evaluation Tool using the Real-Time Delphi method. A panel of 10 experts in nursing and simulation education participated in two rounds of surveys. The evaluation criteria were derived from the International Nursing Association for Clinical Simulation and Learning Standards of Best Practice and relevant literature. Survey items were refined through expert consensus using content validity ratios and coefficient of variation values. The finalized tool was further enhanced with artificial intelligence (AI)–based evaluation capabilities to support objective and systematic assessment. The tool was registered and patented in the Republic of Korea (Korean Intellectual Property Office Registration No. 10-2024-0051234) to acknowledge its innovation and technical merit.
Results
The process resulted in an evaluation tool comprising eight key domains and 36 items, covering scenario structure, learning objectives, preparation, script development, debriefing, facilitation, expected outcomes, and scenario validity. A Kendall’s coefficient of concordance of 0.739 indicated strong agreement among the experts.
Conclusion
This study successfully developed a standardized and validated tool to improve the reliability and effectiveness of simulation-based education in nursing. The tool addresses a key gap in current educational practices and enhances consistency in evaluating nursing simulation scenarios. Future studies should focus on validating its application across diverse educational environments.

Purpose
This pilot study aimed to develop a virtual reality (VR) nursing simulation for pediatric pneumonia care tailored to the Korean clinical context and evaluate its educational effectiveness for nursing students.
Methods
A single-group pretest–posttest design was employed with 20 senior nursing students from April to May 2025. The intervention was a 70-minute VR nursing simulation of pediatric pneumonia care, structured with pre-briefing, a main scenario, and debriefing. Data were collected on participants’ knowledge of pediatric pneumonia, clinical judgment (Nursing Clinical Judgment Scale), and simulation effectiveness (Simulation Effectiveness Tool-Modified) before and after the intervention. Usability (User Experience Questionnaire) and qualitative feedback were collected after the intervention. Pre-post comparisons were performed using paired t-tests.
Results
The program did not yield a statistically significant change in knowledge scores (p=.893). However, there were significant improvements in the mean scores for clinical judgment (p<.001) and simulation effectiveness (p=.013). A qualitative analysis revealed that, while the participants found the experience immersive and realistic, they also reported operational difficulties, indicating the need for usability improvements.
Conclusion
The VR nursing simulation is a promising pedagogical tool for enhancing nursing students’ clinical judgment and perceived learning effectiveness in a Korean pediatric context. The findings suggest that, while the intervention was effective in improving practical reasoning, future iterations should focus on reinforcing knowledge acquisition and optimizing user experience to maximize the educational impact.

Citations

• ORGANIZATION OF THE EDUCATIONAL PROCESS IN “CLINICAL NURSING IN PEDIATRICS” UNDER WARTIME CONDITIONS ON THE MOODLE PLATFORM: CHALLENGES AND ADAPTATION
  N. I. Makieieva, M. S. Diachenko, V. E. Domnich, V. V. Andrushchenko, V. A. Koval
  Медсестринство.2025; (3-4): 54.     CrossRef

Purpose
This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models.
Methods
Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance.
Results
The random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents’ subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance.
Conclusion
This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.

Citations

• Family structure, adolescent mental health, and the role of advisors in the cultural and social context of South Korea
  Sung Min Kim, Su Kyoung Lee, Jooyoung Chang, Joung Sik Son, Kyae Hyung Kim, Sang Min Park
  Scientific Reports.2026;[Epub]     CrossRef