Capturing New Nurses' Experiences and Supporting Critical Thinking Text Network Analysis of Critical Reflective Journals

This study analyzed the contents of critical reflective journals written by new nurses during their orientations using a text network. This study aimed to find ways to reduce turnover and improve clinical field adaptability among new nurses. The authors analyzed the content of reflective journals written by 143 new nurses from March 2020 to January 2021. Text network analysis was performed using the NetMiner 4.4.3 program. After data preprocessing, frequency of occurrence, degree centrality, closeness centrality, betweenness centrality, and eigenvector community were analyzed. In total, 453 words were extracted and refined, and words with high simple frequency and centrality were "incompetence," "preparation," "explanation," "injection," "time," "examination," and "first try." "Medication" had the highest frequency of occurrence, and "incompetence" was the most important keyword in the centrality analysis. In addition, component analysis and eigenvector community analysis revealed three sub-theme groups: (1) basic nursing skills required for new nurses, (2) insufficient competency, and (3) explanation of nursing work. Significantly, this study is the first to use the text network method to analyze the subjective experiences of the critical reflective journals of new nurses. In conclusion, changes are needed to improve the education system for new nurses and promote efficient sharing of nursing tasks.