Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, China.
Department of Intensive Care Unit, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China.
Nurs Open. 2024 Oct;11(10):e70018. doi: 10.1002/nop2.70018.
This cross-sectional study investigates the factors that contribute to academic resilience among nursing students during COVID-19 pandemic.
A cross-sectional study.
A survey was conducted in a general hospital between November and December 2022. The Nursing Student Academic Resilience Inventory (NSARI) model was used to assess the academic resilience of 96 nursing students. The Boruta method was then used to identify the core factors influencing overall academic resilience, and rough set analysis was used to analyse the behavioural patterns associated with these factors.
Attributes were categorised into three importance levels. Three statistically significant attributes were identified ("I earn my patient's trust by making suitable communication," "I receive support from my instructors," and "I try to endure academic hardship") based on comparison with shadow attributes. The rough set analysis showed nine main behavioural patterns. Random forest, support vector machines, and backpropagation artificial neural networks were used to test the performance of the model, with accuracies ranging from 73.0% to 76.9%.
Our results provide possible strategies for improving academic resilience and competence of nursing students.
本横断面研究旨在探讨新冠疫情期间护理学生学术韧性的影响因素。
横断面研究。
2022 年 11 月至 12 月在一家综合医院进行了一项调查。采用护理学生学术韧性量表（NSARI）模型评估 96 名护理学生的学术韧性。然后使用 Boruta 方法识别影响整体学术韧性的核心因素，并使用粗糙集分析分析与这些因素相关的行为模式。
根据与影子属性的比较，将属性分为三个重要性级别。基于比较，确定了三个具有统计学意义的属性（“通过进行适当的沟通赢得患者的信任”“我从我的导师那里获得支持”和“我努力忍受学术困难”）。粗糙集分析显示了九个主要的行为模式。随机森林、支持向量机和反向传播人工神经网络用于测试模型的性能，准确率在 73.0%至 76.9%之间。
我们的研究结果为提高护理学生的学术韧性和能力提供了可能的策略。
