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.

Purpose: This study aimed to describe the process of developing a validated pediatric nursing simulation scenario template using the real-time Delphi method.Methods: A panel of 13 pediatric nursing experts participated in a real-time Delphi survey conducted over two rounds. Initially, 83 items were included in the questionnaire focusing on the structure and content of the simulation scenario template. Data analysis involved calculating the content validity ratio (CVR) and the coefficient of variation to assess item validity and stability.Results: Through iterative rounds of the Delphi survey, a consensus was reached among the experts, resulting in the development of a pediatric nursing simulation scenario template comprising 41 items across nine parts. The CVR values ranged from 0.85 to 1.0, indicating a high consensus among experts regarding the inclusion of all items in the template.Conclusion: This study presents a novel approach for developing a pediatric nursing simulation scenario template using real-time Delphi methods. The real-time Delphi method facilitated the development of a comprehensive and scientifically grounded pediatric nursing simulation scenario template. Our template aligns with the International Nursing Association for Clinical Simulation and Learning standards, and provides valuable guidance for educators in designing effective simulation scenarios, contributing to enhanced learning outcomes and better preparation for pediatric clinical practice. However, consideration of cultural and contextual adaptations is necessary, and further research should explore alternative consensus criteria.

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 systematic review evaluates the effectiveness of medication self-management education programs (MSEP) for older adults by analyzing the core characteristics and reported outcomes of successful interventions. Guided by King’s Goal Attainment Theory, this study identifies which program elements, such as scope, provider, and duration, are associated with positive clinical and psychosocial outcomes, aiming to provide evidence-based recommendations for practice and future research amid rising polypharmacy and inappropriate medication use. Methods: A systematic review searched seven databases (up to September 3, 2024) for randomized controlled trials and quasi-experimental studies of medication self-management education for adults 65+. Inclusion followed PICOTS-SD (Participants, Intervention, Comparison, Outcomes, Time, Setting, and Study Design). Quality was assessed (RoB 2.0, ROBINS-I). Data were narratively synthesized, mapping program characteristics to King’s theory (e.g., interaction, transaction) to analyze goal attainment pathways. Results: The 34 included studies demonstrated that MSEP are broadly effective in improving both clinical and psychosocial outcomes. Specifically, comprehensive, multi-session interventions were frequently associated with significant improvements in psychosocial outcomes such as quality of life and self-efficacy, particularly when delivered by multidisciplinary teams or nurses. In contrast, medication-specific programs (14 studies), often led by pharmacists, proved highly effective in enhancing key clinical outcomes, including medication adherence and the reduction of drug-related problems. Conclusion: MSEP effectively enhance adherence. In accord with King’s Goal Attainment Theory, effectiveness is achieved via a ‘transactional’ process. Programs strong in ‘interaction’ (multi-session, nurse-led) achieve psychosocial ‘goal attainment’, while ‘action’-focused programs (pharmacist-led, specific) achieve clinical ‘goals’. The theory may underscore the need for tailored interventions.