目的 探究护生同情他人、自我同情与正念、心理韧性和心理健康变量的横断面网络结构特征。方法 采用横断面研究设计,2024年4—5月便利选取1 105名护生作为研究对象,采用一般资料调查表、中文版同情动机与行为量表、心理韧性量表、广泛性焦虑症状筛查量表、9条目病人健康问卷和费城正念量表进行调查。采用R语言进行高斯图形模型估计,并通过可视化网络展示节点及边的关系;计算强度中心性和预期影响系数以量化节点的重要性,并通过差异性检验探究网络中核心边缘节点的差异,自助抽样法用于估计网络的稳定性和准确性。同时,通过置换检验,从整体结构、节点和边缘3个角度探讨不同学历层次及实习经历护生的网络差异。结果 节点强度中心性的相关稳定性系数为0.750,自助法得到的边权值95%置信区间较窄。自我同情的网络中,痛苦忍耐度具有最高的强度中心性（1.287）和预期影响系数（1.145）。此外,“焦虑—抑郁”和“心理韧性—正念觉察力”边缘的权重系数分别为0.49和0.40。不同学历及是否有实习经历的护生网络特征无差异,整体网络结构、节点强度及边缘不变性检验的P值均>0.05,仅专科生与研究生组之间存在2条差异边（P值分别为0.001和0.017）。结论 痛苦忍耐度、心理韧性和正念觉察能力在促进护生心理健康中起着关键作用。建议在护理课程中设计模拟场景提升护生的痛苦忍耐阈值,通过情感互动模块和多样化教学活动强化同情意图到行为的转化路径,并在临床实践中引入反思日志和冥想训练等方法增强护生的心理韧性和觉察能力。
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.
关键词
网络分析 /
护生 /
同情 /
自我同情 /
心理健康 /
护理教育
Key words
network analysis /
nursing student /
compassion /
self-compassion /
mental health /
nursing education
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基金
全国医学教育发展中心2023年立项项目（2023B161）
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