Zhao Jiukai, Wu Yibo, Xu Jiayi, Du Kunshuo, Yang Yu, Zang Shuang
Department of Community Nursing, School of Nursing, China Medical University, Shenyang, Liaoning Province, China.
School of Public Health, Peking University, Beijing, China.
BMC Nurs. 2025 Jul 1;24(1):793. doi: 10.1186/s12912-025-03472-4.
In today's fast paced society, the phenomenon of rest intolerance has emerged as a prevalent psychological concern, which has been shown to pose potential risks to the mental and physical health of many people. However, rest intolerance remains underexplored, particularly among nursing students who face high academic pressure.
This study aims to investigate rest intolerance and its associated factors among nursing students using the social ecological model, and to identify central indicators for intervention targets that alleviate this issue through network analysis.
This research is a nationwide cross-sectional study using network analysis. From June 23 to September 29, 2024, the Rest Intolerance Scale-8 (RIS-8) was administered to evaluate rest intolerance among 1,878 nursing students in China. Data collection was conducted using stratified and quota sampling methods. Based on the social ecological model, factors potentially associated with rest intolerance were identified and subsequently investigated using random forest classification. In addition, to further elucidate factors associated with rest intolerance, univariate and multivariable generalized linear models were conducted. Finally, network analyses were undertaken to evaluate central indicators of rest intolerance and core factors among nursing students. Central indicators were identified based on their expected influence value. A higher expected influence value signifies a greater importance of the nodes in the network. The network's stability was assessed using a case-dropping procedure.
The self-reported level of rest intolerance indicated a median score of 24 points. The results of the multivariate generalized linear model analysis demonstrated that nursing students exhibiting higher scores of the study variables are associated with increased feelings of rest intolerance, including higher levels of online social networking addiction (β = 0.32; 95% CI = 0.26 to 0.38), depressive symptoms (β = 0.19; 95% CI = 0.09 to 0.28), perceived stress (β = 0.38; 95% CI = 0.24 to 0.51), and anxiety symptoms (β = 0.45; 95% CI = 0.17 to 0.74). Individuals with higher levels of health literacy (β = -0.17; 95% CI = -0.31 to -0.03) demonstrated a reduced likelihood of experiencing rest intolerance. In addition, the statement "When I am resting or having fun, I feel guilty" from the RIS-8 exhibited the greatest expected influence value within the network. Central indicators of anxiety and depressive symptoms were identified in the network configuration related to rest intolerance and its associated factors.
Nursing students achieved a score of 60% of the total maximum on the Rest Intolerance Scale. Nursing educators and policymakers should fully consider the identified associated factors of this study when designing nursing education programs and relevant policies. Central indicators can be treated as intervention targets to effectively alleviate nursing students' feelings of rest intolerance and promote their mental health.
Not applicable.
在当今快节奏的社会中，不耐休息现象已成为一种普遍的心理问题，已证明这会对许多人的身心健康构成潜在风险。然而，不耐休息现象仍未得到充分研究，尤其是在面临高学业压力的护理专业学生中。
本研究旨在运用社会生态模型调查护理专业学生中的不耐休息现象及其相关因素，并通过网络分析确定缓解该问题的干预目标的核心指标。
本研究是一项采用网络分析的全国性横断面研究。2024年6月23日至9月29日，使用《不耐休息量表-8》（RIS-8）对中国1878名护理专业学生的不耐休息情况进行评估。数据收集采用分层配额抽样方法。基于社会生态模型，确定可能与不耐休息相关的因素，随后使用随机森林分类法进行调查。此外，为进一步阐明与不耐休息相关的因素，进行了单变量和多变量广义线性模型分析。最后，进行网络分析以评估护理专业学生中不耐休息的核心指标和核心因素。根据预期影响值确定核心指标。预期影响值越高，表明网络中节点的重要性越大。使用案例剔除程序评估网络的稳定性。
自我报告的不耐休息水平中位数得分为24分。多变量广义线性模型分析结果表明，研究变量得分较高的护理专业学生与更高的不耐休息感相关，包括更高水平的网络社交成瘾（β = 0.32；95%置信区间 = 0.26至0.38）、抑郁症状（β = 0.19；95%置信区间 = 0.09至0.28）、感知压力（β = 0.38；95%置信区间 = 0.24至0.51）和焦虑症状（β = 0.45；95%置信区间 = 0.17至0.74）。健康素养水平较高的个体（β = -0.17；95%置信区间 = -0.31至-0.03）出现不耐休息的可能性降低。此外，RIS-8中的陈述“当我休息或娱乐时，我会感到内疚”在网络中表现出最大的预期影响值。在与不耐休息及其相关因素相关的网络配置中确定了焦虑和抑郁症状的核心指标。
护理专业学生在不耐休息量表上的得分达到总分最大值的60%。护理教育工作者和政策制定者在设计护理教育计划和相关政策时应充分考虑本研究中确定的相关因素。核心指标可作为干预目标，以有效缓解护理专业学生的不耐休息感并促进其心理健康。
不适用。
