使用机器学习预测南非环境中的护士的弹性
Jennifer Chipps1, Amanda Cromhout1, Umit Tokac2
1School of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7441, South Africa.
概括
这项研究确定了利用机器学习预测南非护士性的因素. 一个简短的弹性选工具准确地预测了护理人员的高与低弹性水平.
科学领域:
- 护理 护理 护理
- 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:
- 护士是压力很高的职业,影响心理健康.
- 弹性,一种积极的应激反应,对心理健康至关重要.
- 了解性因素对于支持护士的心理健康至关重要.
研究的目的:
- 确定南非护士的高与低弹性预测因素.
- 利用机器学习来分析二次调查数据.
- 评估一个简短的弹性选工具的有效性.
主要方法:
- 从对1134名南非护士的五项调查 (2022-2023) 中进行的二次数据分析.
- 随机森林分析应用于人口统计学变量,多年的经验和4项弹性屏幕.
- 预测建模以确定与弹性水平相关的因素.
主要成果:
- 随机森林模型实现了86.41%的适应性级别的分类准确度.
- 人口变量提供了有限的额外预测价值.
- 一个简短的4项弹性选显示了高的预测准确性.
结论:
- 一个简短的弹性选工具有效地识别护士的弹性水平.
- 机器学习模型可以准确地预测护理人口的弹性.
- 进一步的研究可以改进干预措施,以提高护士的弹性.
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