Related Experiment Video
Updated: Apr 9, 2026

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
Published on: January 17, 2025
Latent profile analysis of self-efficacy in information security and positive coping level among nursing students: a
Yingxue Niu1,2, Shiming Chen1,2, Min Hu1,2
1Department of Nursing, Anhui University of Chinese Medicine, Hefei, China.
Objective:
This study aimed to (1) identify latent profiles of nursing students based on self-efficacy in information security and positive coping, and (2) examine factors associated with self-efficacy in information security.
Methods:
A cross-sectional study was conducted on nursing students. Latent profile analysis was used to identify potential profiles based on self-efficacy in information security and positive coping levels. Subsequently, multivariate linear regression was conducted to identify factors associated with the self-efficacy in information security.
Results:
A total of 1,832 nursing students participated in this study. Four distinct latent profiles were identified: low self-efficacy-negative coping profile (24.0%), low self-efficacy-positive coping profile (10.6%), awareness-dominant profile (56.2%), and high self-efficacy-positive coping profile (9.2%). Multivariate linear regression analysis further revealed that education level, grade, clinical internship experience, completion of a nursing informatics course, receipt of information security training, and experience of digital victimization were associated with self-efficacy in information security (P < 0.05).
Conclusion:
The findings revealed heterogeneous profiles and identified factors associated with self-efficacy in information security among nursing students. These results underscore the importance of developing tailored interventions for this population.
Related Concept Videos
Self-Efficacy
Self-Presentation: Self-Monitoring and Self-Handicapping
Sources of Self-Esteem II: Performance Feedback
Protecting Self-Esteem
Self-Esteem and Culture
Self-Serving Bias