Identification of adaptation subgroups among Chinese nursing students: an integrated latent profile and network intervention simulation analysis
AI Summary
Three distinct adaptation profiles among Chinese nursing students: Highly Adaptive (23.4%), Moderately Adaptive (28.7%), and High-Risk (48.0%), differing in well-being, stress, and EI.
Network analysis showed High-Risk subgroup had greatest connectivity, Highly Adaptive subgroup exhibited sparse stable networks, with education stress acting as a key bridge.
Emotional intelligence nodes were most central across profiles and simulated interventions identified EI as primary therapeutic and preventive target, especially for High-Risk students.
BACKGROUND: Nursing students face considerable psychological demands in both academic and clinical training settings, which may constitute a significant source of psychological distress and give rise to distinct adaptation profiles. However, few studies have addressed these issues. This study employs an integrated analytical approach to uncover heterogeneous adaptation patterns and their network characteristics, and further simulates node-specific interventions across different subgroups.
METHODS: This cross-sectional study included 1,126 Chinese nursing students. Measures included mental well-being, emotional intelligence (EI), and high education stress. Latent profile analysis (LPA) identified psychological adaptation subgroups. Gaussian graphical models estimated network structures for each subgroup, followed by centrality and bridge analyses. Ising-based simulations evaluated intervention effects of specific nodes, ranking targets by therapeutic or preventive potential.
RESULTS: Among these students, LPA identified three distinct subgroups: Highly Adaptive (23.4%), Moderately Adaptive (28.7%), and High-Risk (48.0%). The profiles differed significantly in mental well-being, education stress, and EI (all P < 0.001). Network analysis showed that the High-Risk subgroup had the highest connectivity, while the Highly Adaptive subgroup was more sparse and stable. EI-related nodes displayed the greatest centrality across all profiles, and the education stress node consistently functioned as a key bridge. Intervention simulations revealed subgroup-specific sensitivity patterns. For High-Risk students, EI nodes served as the most influential therapeutic and preventive targets, with a marked asymmetry ratio. The Moderately and Highly Adaptive profiles demonstrated distinct patterns of intervention efficiency and resilience.
CONCLUSION: This study identifies distinct psychological adaptation profiles among nursing students and highlights EI as a critical intervention target, especially for High-Risk subgroups. The integrated analytical framework provides a replicable approach for personalized mental health research.
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