Cross-sectional network analysis of compassion for others and self-compassion in nursing students

LIU Jingting; FU Haichao; LIN Yuanhong; GAO Yulin

doi:10.16460/j.issn2097-6569.2025.11.001

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Journal of Nursing ›› 2025, Vol. 32 ›› Issue (11) : 1-6. DOI: 10.16460/j.issn2097-6569.2025.11.001

Cross-sectional network analysis of compassion for others and self-compassion in nursing students

LIU Jingting, FU Haichao, LIN Yuanhong, GAO Yulin

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Abstract

Objective To explore the cross-sectional network structure characteristics of nursing students' compassion for others, self-compassion, mindfulness, resilience, and mental health variables. Methods A cross-sectional study was conducted in 1,105 nursing students from April to May 2024. Data were collected using General Information Questionnaire, the Chinese version of the Compassionate Motivation and Action Scales, the 10-item Connor-Davidson Resilience Scale, the Generalized Anxiety Disorder-7 Item Scale, the Patient Health Questionnaire-9, and the Philadelphia Mindfulness Scale. Gaussian graphical model estimation was performed using R language. Node and edge relationships were visualized, and node importance was quantified by calculating strength centrality and expected influence. Core nodes and edges were identified through difference tests, and network stability and accuracy were estimated using bootstrapping. Permutation tests were used to examine network differences in nursing students with different internship experiences and educational levels from three perspectives: overall structure, nodes, and edges. Results The correlation stability coefficient for node strength centrality was 0.750, and the bootstrap-derived edge weight 95% confidence intervals were narrow. In the self-compassion network, distress tolerance had the highest strength centrality (1.287) and expected influence index (1.145). Additionally, the edge weights for "anxiety-depression" and "psychological resilience-mindful awareness" were 0.49 and 0.40, respectively. There were no significant differences in network characteristics comparison of nursing students with different educational background or internship experiences (P>0.05 for the overall network structure, node strength, and edge invariance), except for two different edges between college student group and graduate group (P=0.001 and P=0.017 respectively). Conclusion Distress tolerance, resilience, and mindful awareness are crucial for nursing students' mental health. It is recommended to design simulation scenarios in nursing curricula to increase students' distress tolerance, strengthen the conversion path from compassionate intention to action through emotional interactive modules and diverse teaching activities, and enhance resilience and mindfulness in clinical practice by introducing reflection diaries and meditation training in clinical practice.

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network analysis / nursing student / compassion / self-compassion / mental health / nursing education

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LIU Jingting, FU Haichao, LIN Yuanhong, GAO Yulin. Cross-sectional network analysis of compassion for others and self-compassion in nursing students[J]. Journal of Nursing. 2025, 32(11): 1-6 https://doi.org/10.16460/j.issn2097-6569.2025.11.001

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