在护理学生中建立和验证同情疲劳的预测模型
Huiling Zhang1,2, Wireen Leila Dator3,4
1Laboratory of Geriatric Nursing and Health, Anhui University of Traditional Chinese Medicine, Hefei, China.
BMC nursing
|February 19, 2025
概括
护理学生的同情疲劳可以使用逻辑回归模型来预测. 关键因素包括心理性,同行支持,二次创伤和同情满意度,有助于早期干预.
科学领域:
- 护理教育 护理教育
- 心理健康 心理健康
- 医疗保健中的机器学习
背景情况:
- 同情疲劳在护理学生中很普遍,这是因为他们接触到患者的痛苦.
- 由于经验不足和情绪调节较弱,学生更容易受到伤害.
- 早期识别同情疲劳对于学生的福祉和护理质量至关重要.
研究的目的:
- 开发一个对护理学生同情疲劳的预测模型.
- 确定影响同情疲劳的关键因素.
- 为教育工作者和管理人员提供证据,以支持护理学生.
主要方法:
- 在512名护理学生中进行了横截面调查.
- 变量选择的拉索回归.
- 后勤回归,随机森林和XGBoost模型用于预测.
- 用于特征重要性解释的SHAP值.
主要成果:
- 物流回归模型实现了0.77.7的AUC.
- 确定了关键预测因素:心理性,同行支持,二次创伤和同情满意度.
- 该模型展示了对同情疲劳的良好预测性能.
结论:
- 为护理学生同情疲劳开发了一个强大的预测模型.
- 鉴定出来的因素为有针对性的早期干预提供了基础.
- 结果支持加强护理学生的心理健康和管理策略.
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