Journal of Korean Academy of Nursing最新文献

Purpose: This review compares the development of South Korea's Advanced Practice Registered Nurse (APRN) system the well-established APRN system in the United States and provides recommendations for future improvements to the APRN system in South Korea.

Methods: To compare the APRN systems between the two countries, an integrative literature review was conducted using multiple databases and professional nursing organization documents and reports from both the United States and South Korea.

Results: Issues were identified in five major domains: (1) research evidence, (2) education and training, (3) the scope of practice, (4) financial mechanisms, and (5) public awareness and acceptance.

Conclusion: Recommendations are made in four areas: (1) building evidence to support APRN programs; (2) strengthening APRN education; (3) establishing legal support and reimbursement mechanisms; and (4) improving public awareness and acceptance of APRNs.

Purpose: This study aimed to develop and validate a machine learning-based prediction model for early hospital readmission (EHR) post-kidney transplantation.

Methods: The study was conducted at the organ transplantation center of a university hospital, utilizing data from 470 kidney transplant recipients. We built and trained four machine learning models and tested them to identify the strongest EHR predictors. Predictive performance was evaluated using confusion matrices and the area under the receiver operating characteristic curve (ROC AUC).

Results: Among the 470 kidney transplant recipients with a mean age of 46.1 ± 12.02 years, 322 (68.5%) were males, and 74 (15.7%) were readmitted within 30 days after kidney transplantation. In total, 241 (51.2%) recipients were found to have experienced EHR after applying the random over-sampling examples method. The random forest model achieved the best performance, with an ROC AUC of .87 (validation set) and .82 (test set). The 15 most important features were steroid pulse therapy (recipient), cerebrovascular accident (recipient), heart failure (recipient), male sex (donor), cardiovascular disease (recipient), weekend discharge (recipient), peritoneal dialysis (recipient) cerebrovascular accident as the cause of brain death (donor), current smoker (recipient), cardiac arrest (donor), previous kidney transplantation (recipient), age (donor), hypertension (donor), male sex (recipient), and dialysis duration (recipient).

Conclusion: Our framework demonstrated strong predictive interpretability. It can support appropriate and effective clinical decision-making by assisting transplant professionals in stratifying recipients based on their risk of EHR. prioritizing post-discharge care and follow-up for high-risk individuals, and allocating targeted interventions such as closer monitoring or education.

Purpose: This study aimed to develop a machine learning model to predict exclusive breastfeeding during the first 3 months after birth and to explore factors affecting breastfeeding outcomes.

Methods: Data from 2,579 participants in the Korean Early Childhood Education & Care Panel between March 1 and June 3, 2025 were analyzed using Python version 3.12.8 and Colab. The dataset was split into training and testing sets at an 80:20 ratio, and five classifiers (random forest, logistic regression, decision tree, AdaBoost, and XGBoost) were trained and evaluated using multiple performance metrics and feature importance analysis.

Results: The confusion matrix of the random forest classifier model demonstrated strong performance, with a precision of 86.6%, accuracy of 84.8%, recall of 96.8%, F1-score of 91.9%, and an area under the curve of 86.0%. Twenty-one features were analyzed, from which feeding plan, breastfeeding at 1 month, marriage period, maternal prenatal weight, self-respect, alcohol consumption, grit, value placed on children, maternal age, and depression emerged as important predictors of exclusive breastfeeding in the first 3 months.

Discussion: A robust model was developed to predict exclusive breastfeeding that identified feeding planning and breastfeeding at 1 month as the most influential predictors. The model could be implemented in clinical and community settings to guide tailored breastfeeding support strategies, coupled with the integration of maternal self-respect, grit, and the value placed on children in counseling programs to promote exclusive breastfeeding.

Purpose: This study aimed to synthesize existing evidence on digital health literacy (DHL) among older adults and to estimate the associations between related influencing factors through a systematic literature review and meta-analysis.

Methods: A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Literature searches were performed across PubMed, EMBASE, Cochrane Library, CINAHL, RISS, and DBPIA. The search and screening process was conducted from December 24, 2023, to March 31, 2025. Effect sizes (ESr) using correlation coefficient for each variable were calculated, and meta-analyses were performed using Microsoft Excel and R version 4.3.1.

Results: Forty-seven variables were identified, including two demographic, six physical, six behavioral, 23 psychosocial, and 10 cognitive factors. Meta-analysis results showed that physical, behavioral, psychosocial, and cognitive factors had significant effects on DHL. Among these, digital information level (ESr=.62; 95% confidence interval [CI], 0.55 to 0.69) within the cognitive domain and technophobia (ESr=-.55; 95% CI, -0.47 to -0.40) within the psychosocial domain demonstrated the largest ESr.

Conclusion: Among factors influencing DHL, digital information level and technophobia showed the strongest associations. These findings suggest that improving DHL in older adults requires a dual approach targeting both cognitive and psychosocial dimensions-enhancing digital information skills while reducing technophobia-to effectively support digital engagement and health empowerment in this population (PROSPERO registration number: CRD42023487486).

Purpose: This study aimed to propose strategies for strengthening the nursing workforce by expanding their roles as advanced practice providers (APPs).

Methods: A mixed-methods approach was employed, consisting of five focus group interviews (FGIs) with 30 healthcare professionals (including 10 physicians) and a two-round Delphi survey with 49 experts. The FGIs explored practical insights from clinical settings, while the Delphi process validated and prioritized strategic recommendations through expert consensus.

Results: Four major themes emerged from the FGI analysis: (1) utilization of diverse APPs to ensure quality care, (2) expanding the scope of practice of APPs, (3) requirements to ensure the quality of APPs, and (4) strategies for sustainable management of the APP workforce. Building on these findings, the Delphi survey identified five strategic domains: "definition and qualifications," "scope of practice," "educational programs," "credentialing and regulation," and "support systems." Key areas of consensus included the need for mandatory clinical experience and specialty training, legal clarification of role boundaries, standardized curricula with certification mechanisms, and institution-led support systems such as task-specific job descriptions and recredentialing processes.

Conclusion: To effectively strengthen APP roles, it is essential to build on the existing advanced practice nurse (APN) framework, which already includes structured curricula and national certification. Furthermore, integrative strategies should be developed to incorporate experienced clinical nurses without APN licenses into the APN system.

Purpose: This study aimed to develop a scale to measure end-of-life care (EOLC) competency among nurses working in long-term care hospitals and to evaluate its validity and reliability.

Methods: Preliminary items were developed based on attributes and indicators identified through a conceptual analysis of EOLC competency. The initial version of the scale was refined through expert content validity assessment, item revision, and a pilot test. The main survey was conducted among 460 nurses in long-term care hospitals, and 409 valid responses were analyzed after excluding 51 incomplete or invalid cases. Data were analyzed using software-assisted item analysis, exploratory and confirmatory factor analyses, and assessments of convergent, discriminant, and criterion-related validity, as well as reliability testing.

Results: The initial 55 items were reduced to a final set of 30 items across seven dimensions. Model fit indices indicated good construct validity (χ²/degrees of freedom=1.91, standardized root mean square residual=.06, root mean square error of approximation=.07, Tucker-Lewis index=.90, comparative fit index=.91), with a total explained variance of 70.2%. The scale demonstrated strong criterion-related validity (r=.76, p<.001), high internal consistency (Cronbach's α=.95; McDonald's ω=.95), acceptable test-retest reliability (r=.56, p<.001), and an intraclass correlation coefficient of .72 (95% confidence interval, .51-.84; p<.001).

Conclusion: The developed scale is a valid and reliable instrument for assessing EOLC competency among nurses in long-term care hospitals. It can be effectively utilized for educational assessment, training evaluation, and the measurement of program effectiveness in end-of-life care.

Purpose: This study aimed to investigate the two-mediator serial mediation effect of missed nursing care and job satisfaction on the relationship between presenteeism and turnover intention in clinical nurses.

Methods: A cross-sectional predictive correlational study was conducted, and the participants were 208 clin-ical nurses working in advanced general hospitals in South Korea. Data were collected from October 6 to November 7, 2023 using self-reported questionnaires, including general characteristics, presenteeism, missed nursing care, job satisfaction, and turnover intention. Data were analyzed using IBM SPSS/WIN ver. 29.0 and PROCESS macro ver. 4.2.

Results: Missed nursing care and job satisfaction exhibited a double mediating effect on the relationship between presenteeism and clinical nurses' turnover intention. In addition, missed nursing care showed a mediating effect on the relationship between presenteeism and clinical nurses' turnover intention. Job satisfaction had a mediating effect on the relationship between presenteeism and clinical nurses' turnover intention. Presenteeism had a direct effect on missed nursing care, job satisfaction, and turnover intention. Missed nursing care exerted a direct effect on job satisfaction and turnover intention among clinical nurses. Job satisfaction had a direct effect on turnover intention.

Conclusion: To reduce nurses' turnover intention, it is essential to develop and implement programs focused on preventing presenteeism. Additionally, organizational initiatives should prioritize active support for nurses' health management, alleviating the shortage of nursing staff, augmenting job satisfaction, and improving the overall working environment.

Purpose: This study aimed to identify risk factors associated with the readmission of patients with diabetic ketoacidosis (DKA) through a systematic review and meta-analysis.

Methods: A systematic literature review was conducted in accordance with the PRISMA guidelines. Relevant studies were retrieved from international databases (PubMed, EMBASE, Cochrane Library, CINAHL, PsycINFO, and Web of Science) and Korean databases (RISS, KoreaMed, KMbase, KISS, and DBpia). Study quality was evaluated using the Newcastle-Ottawa Scale. Meta-analysis was performed using a random-effects model with the Hartung-Knapp-Sidik-Jonkman adjustment to account for the limited number of studies and heterogeneity.

Results: Fifteen studies were included in the review, and eight were eligible for meta-analysis. From the systematic review, 21 risk factors for DKA readmission were identified and categorized into five domains: demographic, socioeconomic, diabetes-related, comorbidity, and health-behavioral factors. In the meta-analysis, significant risk factors included low income, psychiatric disorders, and discharge against medical advice.

Conclusion: This study demonstrates that DKA readmissions result from the complex interplay of multiple clinical and social factors. By identifying these risk factors and suggesting risk-stratification criteria, the findings may support the development of tailored interventions, such as self-management education, integrated mental health care, structured discharge planning, and coordinated post-discharge follow-up.

Purpose: This study compared trends in research designs and keywords by analyzing the abstracts of four major nursing journals over the past decade, focusing on the Journal of Korean Academy of Nursing (JKAN) in comparison with the International Journal of Nursing Studies (IJNS), Journal of Advanced Nursing (JAN), and Japan Journal of Nursing Science (JJNS).

Methods: A bibliometric analysis was conducted, encompassing 5,522 abstracts published between 2015 and 2024. Research designs were first classified as "quantitative," "qualitative," or "other," and then further sub-classified based on international evidence-based frameworks. Text preprocessing was also conducted, and term frequency-inverse document frequency was applied to evaluate keyword importance. The 2015-2019 and 2020-2024 periods were compared to examine changes in both research designs and keyword importance.

Results: Compared to IJNS, JAN, and JJNS, JKAN published more instrument development and analytic studies but fewer randomized controlled trials and systematic reviews. Over time, the number of instrument development and mixed-methods studies in JKAN increased, while high-evidence designs remained scarce. Keyword analysis showed JKAN's emphasis on psychosocial themes such as self-efficacy, quality of life, and depression, whereas the other journals more often highlighted policy- and institution-related topics. Across journals, COVID-19 and patient safety emerged as important themes after 2020.

Conclusion: JKAN demonstrates strengths in methodological diversity within quantitative research and in digital health-related analytics. However, high-evidence study designs and policy-oriented keywords are underrepresented in JKAN. Strategic expansion toward randomized controlled trials, systematic review, global and digital health, and policy-relevant research is recommended to strengthen JKAN's international competitiveness.