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"Shin Hyang Kim"

Review Article
A Systematic Review and Meta-Analysis of Information Literacy Competencies and Associated Variables among Korean Nursing Students
Shin Hyang Kim, Jong Mi Lim
J Korean Acad Nurs Adm 2025;31(4):358-370.   Published online September 30, 2025
DOI: https://doi.org/10.11111/jkana.2024.0061
Purpose
The purpose of this study is to provide academic basic data by quantitatively integrating variables related to information literacy competence of Korean nursing students. Methods: This study is a meta-analysis study to systematically review the research on information literacy competence of Korean nursing students and related variables to identify the effectiveness of each variable group. The selection criteria for the analysis subjects were determined using the PICOS framework in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) systematic review reporting guidelines. Theses and journal articles published from September 1, 2024, to September 30, 2024, were searched. Results: The effect sizes of the sub-variables of each variable group showed that the personal thinking competence group showed the largest effect size in the order of problem-solving ability (Fisher’s Z=.90) and critical thinking ability (Fisher’s Z=.57); the professional competence group showed evidence-based practice (Fisher’s Z=.84), clinical performance (Fisher’s Z=.65), learning engagement (Fisher’s Z=.51), communication ability (Fisher’s Z=.49), and academic achievement (Fisher’s Z=.45); and the professional competence group showed privacy awareness (Fisher’s Z=.51), professionalism (Fisher’s Z=.42), and ethical awareness (Fisher’s Z=.41). Conclusion: Based on the results of this study, it is necessary to develop a program to strengthen the information literacy competence of nursing students. Furthermore, there is a need to develop curricula and teaching-learning strategies to enhance information utilization capabilities, along with sustained attention to practice-based educational models tailored to AI and digital environments.
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