Nurse education in practice · 2025
Exploring digital resilience and its related factors among nursing students in the era of AI.
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Su Ya, Yu Zhiyuan, Chen Si, Chen Jing, Wang Lin
Аннотация
AIM: This study aims to identify distinct subgroups of digital resilience among nursing students and examine the factors associated with these subgroups.
BACKGROUND: Digital resilience, the ability to adapt to technological changes and overcome challenges in higher education, is crucial for protecting students' psychological health and improving academic performance. In the context of Artificial Intelligence (AI) and digital transformation in nursing education, this resilience is essential for students to navigate virtual learning and integrate advanced technologies into their practice.
DESIGN: A cross-sectional study.
METHODS: This study was conducted in eight universities in China guided by ecological systems theory and nursing students were recruited through convenience sampling. Latent profile analysis (LPA) identified subgroups and logistic regression examined related factors.
RESULTS: A total of 331 (81.73 %) participants were included in the final analysis. The average age of participants was 20.41SD0.67 years, with 283 female (85.55 %). Latent profile analysis revealed two subgroups: the "High Digital Resilience Group" (n = 278, 83.99 %) and the "Low Digital Resilience Group" (n = 53, 16.01 %). Participants who were male (OR = 3.47, p = 0.02), had low household income (OR = 0.23, p = 0.01, low professional identity (OR = 0.86, p < 0.001) and low friend support (OR = 0.82, p < 0.001) were more likely to belong to the low digital resilience group.
CONCLUSION: Educators should focus on enhancing students' professional identity and providing social support, especially for those with low digital resilience. The findings provide practical guidance for integrating AI into nursing education to enhance digital resilience.
BACKGROUND: Digital resilience, the ability to adapt to technological changes and overcome challenges in higher education, is crucial for protecting students' psychological health and improving academic performance. In the context of Artificial Intelligence (AI) and digital transformation in nursing education, this resilience is essential for students to navigate virtual learning and integrate advanced technologies into their practice.
DESIGN: A cross-sectional study.
METHODS: This study was conducted in eight universities in China guided by ecological systems theory and nursing students were recruited through convenience sampling. Latent profile analysis (LPA) identified subgroups and logistic regression examined related factors.
RESULTS: A total of 331 (81.73 %) participants were included in the final analysis. The average age of participants was 20.41SD0.67 years, with 283 female (85.55 %). Latent profile analysis revealed two subgroups: the "High Digital Resilience Group" (n = 278, 83.99 %) and the "Low Digital Resilience Group" (n = 53, 16.01 %). Participants who were male (OR = 3.47, p = 0.02), had low household income (OR = 0.23, p = 0.01, low professional identity (OR = 0.86, p < 0.001) and low friend support (OR = 0.82, p < 0.001) were more likely to belong to the low digital resilience group.
CONCLUSION: Educators should focus on enhancing students' professional identity and providing social support, especially for those with low digital resilience. The findings provide practical guidance for integrating AI into nursing education to enhance digital resilience.