Journal of Korean Academy of Nursing

Articles in E-pub version are posted online ahead of regular printed publication.

Research Papers

Effects of presenteeism on turnover intention in clinical nurses through the serial mediating roles of missed nursing care and job satisfaction: a cross-sectional predictive correlational study: Hyeonseon Cheon, Seok Hee Jeong, Hyun Kyung Kim, Hyoung Eun Chang; Received February 6, 2025 Accepted August 28, 2025 Published online November 10, 2025; DOI: https://doi.org/10.4040/jkan.25015 [Epub ahead of print]

Abstract PDF ePub: 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 clinical 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.

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Effects of an integrated healthcare program for postpartum women: a quasi-experimental study: Eun Suk Hwang, Ju-Hee Nho; Received May 30, 2025 Accepted September 11, 2025 Published online November 7, 2025; DOI: https://doi.org/10.4040/jkan.25076 [Epub ahead of print]

Abstract PDF ePub: Purpose
This study aimed to develop and evaluate an integrated healthcare program for postpartum mothers based on Cox’s interaction model of client health behavior.
Methods
A non-equivalent control group pretest-posttest design was used. The integrated healthcare program was administered 6 times over 2 weeks to postpartum mothers in the experimental group (n=21), while the control group (n=23) received standard care. Data were collected from June 3 to July 15, 2024, through structured questionnaires measuring postpartum fatigue, depression, marital intimacy, and mother-infant attachment. Analyses were conducted using IBM SPSS ver. 23.0.
Results
The experimental group showed significantly lower postpartum fatigue (Z=–2.00, p=.023), a significantly proportion of improvement in postpartum depression (χ2=10.32, p=.012), and a significant increase in mother-infant attachment (t=1.70, p=.048) compared to the control group. However, there was no significant difference in marital intimacy between groups (Z=–0.46, p=.326).
Conclusion
These results suggest that an integrated health management program including physical health, psychological stability, and relational support can be used as an effective nursing intervention to promote health in postpartum mothers. Therefore, additional research is warranted that expands and applies integrated programs for postpartum mothers in various environments in postpartum care centers and communities.

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Development of a predictive model for exclusive breastfeeding at 3 months using machine learning : a secondary analysis of a cross-sectional survey: Hyun Kyoung Kim; Received June 23, 2025 Accepted September 2, 2025 Published online October 28, 2025; DOI: https://doi.org/10.4040/jkan.25086 [Epub ahead of print]

Abstract PDF ePub: 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.

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