Evidence map›Paper›PMID 42399875›Full record

ArticleBMC nursing2026

Identification of adaptation subgroups among Chinese nursing students: an integrated latent profile and network intervention simulation analysis.

Yao Li, Tian Pan, Jing Li, Lin Zhang

Abstract read
In one paragraph

Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yao LiSchool of Nursing, Wannan Medical University, Wuhu City, An Hui Province, China.
Tian PanSchool of Nursing, Wannan Medical University, Wuhu City, An Hui Province, China.
Jing LiSchool of Nursing, Wannan Medical University, Wuhu City, An Hui Province, China.
Lin ZhangSchool of Nursing, Wannan Medical University, Wuhu City, An Hui Province, China. yaoran2008@163.com.

Funding

High-level Talent Scientific Research Startup Fundation of Wannan Medical College X600100191Natural Science Research Project of Anhui Provincial Department of Education for Universities and Colleges 2025AHGXZK40175
6 · The paper itself

Abstract

backgroundNursing 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.

methodsThis 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.

resultsAmong 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.

conclusionThis 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. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Emotional intelligenceMental well-beingNursing studentSimulation intervention analyses

Identifiers

PMID42399875
PMCPMC13403883

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