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一个基于MLP的预测模型,用于对中国急诊护士工作场所暴力风险评估

Kai Liu¹, XinYue Zhang², Yan Jiang³

¹Department of Emergency Medicine, Shengjing Hospital of China Medical University, Shenyang, China.

|December 9, 2025

概括

紧急护士的职场暴力 (WPV) 风险可以通过人格特征和人口统计数据来预测. 一个多层感知器模型确定了针对性干预的高风险"护理人格".

科学领域:

职业健康职业健康职业健康
心理学心理学心理学
数据科学数据科学数据科学

背景情况:

工作场所暴力 (WPV) 是急诊室护士的重要职业风险.
像性和道德勇气这样的个性特征与WPV脆弱性有关.

研究的目的:

根据个性特征和WPV风险,确定不同的护士子组.
使用人口数据开发WPV风险的预测模型.

主要方法:

一项针对中国465名急诊护士的全国性研究.
无监督集群用于识别个性特征子组.
使用人口特征开发和验证多层感知器 (MLP) 模型.

主要成果:

确定了三个人格集群,一个高风险子组.
在预测WPV风险方面,MLP模型实现了0.711准确度.
该模型在外部验证中证明了可通用性.

结论:

确定了一个易受WPV感染的高风险"护理人格".
使用人口统计数据的MLP模型可以有效地分层WPV风险.
这为职业健康的早期识别和干预提供了一个实用的工具.

关键词:

紧急情况护士机器学习是机器学习.多层的感知子是多层的.人格特征个性特征工作场所暴力工作场所暴力

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