A Text Network Analysis of Nurse Managers’ Feedback Journals

This study employed text network analysis to examine nurse managers' feedback journals for new nurses, highlighting the critical need for structured support during their transition period. A total of 429 feedback journals were documented by 32 nurse managers for 239 new nurses between September 2019 and March 2021 in South Korea, aiming to identify the challenges faced by new nurses and explore keyword relationships and thematic patterns. The analysis process included 4 stages: data preprocessing, keyword extraction, network and centrality analysis, and subtheme analysis. Using NetMiner 4.5.0, the analysis identified several central keywords in the feedback journals, with "performance" showing the highest centrality values (degree centrality = 0.448, closeness centrality = 0.580, betweenness centrality = 0.117), followed by "experience" and "explanation." Through community detection analysis using the eigenvector method, 3 distinct subthemes emerged from the network structure: "clinical practice fundamentals," "learning and development process," and "support and educational system." The findings of this study provide empirical evidence for developing structured feedback programs and enhancing nurse managers' interviewing competencies. The findings provide empirical evidence regarding linguistic patterns and thematic structures in nurse managers' feedback, suggesting systematic documentation protocols and utilization of these insights to inform structured feedback initiatives, thereby supporting new nurses' development.