Childhood and adolescence are crucial periods for developing one’s awareness of sexuality. Comprehensive Sexuality Education (CSE) during these stages is essential for overall growth, fostering healthy self-concepts, and addressing diverse sexual issues among children and adolescents globally. A meta-analysis was conducted to analyze the effectiveness of CSE programs. A literature search was performed on EMBASE, PubMed, CINAHL, Cochrane Library, and PsycInfo for studies published before 14 June 2023, and based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We used the Comprehensive Meta-Analysis (CMA, V4) software version 4.0 for the analysis and interpreted the effect sizes according to Cohen’s definition. Between 2011 and 2020, 21 studies on CSE were published, with the United States having the most publications (17). Of the 34 studies reviewed, 20 were randomized controlled trials. The primary population for CSE was middle/high school students (15), with the most frequent age range being 10–19 years (26). The overall effect size of CSE was significant (effect size = 1.31, p < 0.001), with cognition (effect size = 5.76, p < 0.001) being the most significant. CSE is an effective educational tool for children and adolescents with a significant impact on variables such as cognition and abstinence. It should be incremental from childhood and adolescence to adulthood.

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.

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.

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.

Purpose: This study aims to systematically review the literature and conduct a meta-analysis to thoroughly analyze the outcomes of studies on simulation programs in psychiatric nursing.Methods: We conducted an initial search from January 1, 2000, to September 30, 2023, using databases such as PubMed, EMBASE, CINAHL, the Cochrane Library, and Web of Science. A total of 2,571 articles were reviewed based on inclusion and exclusion criteria. We selected 35 articles for systematic literature review and subjected 20 of them to meta-analysis. Data analysis was conducted using descriptive statistics and the Comprehensive Meta-Analysis program.Results: The number of psychiatric nursing simulation programs has increased since 2015. The most common programs used Standard Patients (SP), but studies using Virtual Reality (VR), audio, video, and other methods have recently been reported. The programs addressed various mental health issues, including depression, suicide, violence, alcohol problems, and mood disorders. Overall, considering all the studies, a significant effect was observed with a pooled Hedges’s g value of 0.56 (95% CI: 0.35~0.78; p<.001).Conclusion: These findings highlight the importance of incorporating simulation-based education into nursing curricula to equip professionals with the skills to provide high-quality care to individuals with mental health problems. Further research is needed to explore the long-term benefits of these educational interventions on patient care.

Purpose: The aim of this study was to identify factors influencing the learning transfer of nursing students in a non-face-to-face educational environment through structural equation modeling and suggest ways to improve the transfer of learning.Methods: In this cross-sectional study, data were collected via online surveys from February 9 to March 1, 2022, from 218 nursing students in Korea. Learning transfer, learning immersion, learning satisfaction, learning efficacy, self-directed learning ability and information technology utilization ability were analyzed using IBM SPSS for Windows ver. 22.0 and AMOS ver. 22.0.Results: The assessment of structural equation modeling showed adequate model fit, with normed χ2=1.74 (P<0.024), goodness-of-fit index=0.97, adjusted goodness-of-fit index=0.93, comparative fit index=0.98, root mean square residual=0.02, Tucker-Lewis index=0.97, normed fit index=0.96, and root mean square error of approximation=0.06. In a hypothetical model analysis, 9 out of 11 pathways of the hypothetical structural model for learning transfer in nursing students were statistically significant. Learning self-efficacy and learning immersion of nursing students directly affected learning transfer, and subjective information technology utilization ability, self-directed learning ability, and learning satisfaction were variables with indirect effects. The explanatory power of immersion, satisfaction, and self-efficacy for learning transfer was 44.4%.Conclusion: The assessment of structural equation modeling indicated an acceptable fit. It is necessary to improve the transfer of learning through the development of a self-directed program for learning ability improvement, including the use of information technology in nursing students’ learning environment in non-face-to-face conditions.