确定初级护士所经历的工作场所暴力的最关键预测因素:可解释的机器学习视角
Lanjun Luo1, Yuze Wu1, Siyuan Li1
1School of Management, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China.
Journal of nursing management
|April 14, 2025
概括
初级护士面临着相当大的工作场所暴力风险. 机器学习模型确定了医疗投诉和专业身份等关键因素,使得更好的风险预测和干预成为可能.
科学领域:
- 护理 护理 护理
- 医疗保健管理的管理
- 在医疗保健中的数据科学.
背景情况:
- 工作场所暴力对初级护士构成严重威胁,由于缺乏经验和较低的专业认可,他们更容易受到伤害.
- 现有的研究缺乏对关键因素的具体分析和针对初级护士工作场所暴力风险预测模型.
研究的目的:
- 用可解释机器学习预测初级护士在工作场所的暴力风险.
- 确定对工作场所暴力的关键影响因素及其非线性影响.
主要方法:
- 一项涉及中国5663名初级注册护士的观察性,横截面研究.
- 利用一种可解释的机器学习框架,将光梯度提升机 (LightGBM) 与快速增量扩展 (SHAP) 和积累局部效应 (ALE) 结合起来.
主要成果:
- 轻GBM模型在预测工作场所暴力方面表现出高准确度 (AUC=0.761).
- 发现的关键预测因素包括经历医疗投诉,心理需求和专业身份.
结论:
- 通过LightGBM-SHAP-ALE方法,有效地识别出处于工作场所暴力高风险的初级护士.
- 调查结果为及时检测和干预策略提供了基础,以减轻工作场所暴力.
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