护理本科生隐性逃课行为的网络分析

doi:10.3761/j.issn.1672-9234.2025.05.010

摘要/Abstract

摘要：

目的探讨护理本科生学习倦怠、手机依赖、自我控制、学习动机、隐性逃课行为之间的关系。方法 2025年1月，采用便利抽样法，选取东北地区某医学院校392名护理本科生作为调查对象，使用大学生隐性逃课量表、学习动机量表、大学生学习倦怠量表、手机依赖指数和大学生自我控制量表对其进行问卷调查。通过R 4.4.1软件构建稀疏化因素网络模型，分析变量间的相关性、节点强度、中介中心性、接近中心性、可预测性，识别核心因素和桥梁因素；并评估网络稳定性。结果护理本科生隐性逃课与学习倦怠（r=0.40）和手机依赖（r=0.27）间呈正相关。隐性逃课可预测性为46.1%；学习倦怠强度为1.90；学习动机中介中心性为3.00，高于其他变量（中介中心性均为0）；隐性逃课和手机依赖的接近中心性较高，分别为0.69和0.71。网络稳定性方面，相关性稳定系数（CS）为0.75。结论学习倦怠是护理本科生隐性逃课的核心因素，学习动机在网络中起关键桥梁作用。护理教育者可针对学习倦怠和学习动机拟定并实施精细化的干预策略，以有效减少护理本科生的隐性逃课行为。

关键词: 教育, 护理, 本科生, 隐性逃课, 网络分析

Abstract:

Objective To explore the interrelationships among learning burnout，mobile phone addiction，self-control，learning motivation，and recessive truancy behavior of nursing undergraduates. Methods A convenience sampling method was used to recruit 392 nursing undergraduates from a medical university in Northeast China as the study participants. The Recessive Truancy Scale for College Students，the Learning Motivation Scale，the Learning Burnout Scale，the Mobile Phone Addiction Index，and the College Student Self-Control Scale was applied in data collection. A sparse factor network model was constructed using R 4.4.1 software to analyze the correlations among variables，node strength，betweenness centrality，and closeness centrality among variables，identifying core and bridging factors. Network stability was also assessed. Results Recessive truancy showed significant positive correlations with learning burnout（r=0.40） and mobile phone addiction（r=0.27）. The predictability of recessive truancy was 46.1%，and the intensity of learning burnout was 1.90. Learning motivation acted as a bridging factor，exhibiting the highest betweenness centrality of 3.00，significantly surpassing other variables. Recessive truancy and mobile phone addiction demonstrated higher closeness centrality with the index of 0.69 and 0.71，respectively. The correlation stability（CS） coefficient was 0.75. Conclusion Learning burnout is the central driver of recessive truancy among nursing undergraduates，while learning motivation serves as a critical bridging factor. Nursing educators should develop and implement targeted interventions focusing on learning burnout and motivation to effectively reduce recessive truancy behaviors in this population.

Key words: Education, Nursing, Undergraduate, Recessive truancy, Network analysis

杨楠, 郭宏, 郭智芮, 宋雪涵, 路文婷. 护理本科生隐性逃课行为的网络分析[J]. 中华护理教育, 2025, 22(5): 573-579.

YANG Nan, GUO Hong, GUO Zhirui, SONG Xuehan, LU Wenting. Network analysis of recessive truancy in undergraduate nursing students[J]. Chinese Journal of Nursing Education, 2025, 22(5): 573-579.

图/表 4

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