使用机器学习来预测护士的创造力:一个多学科的方法
Rola H Mudallal1,2, Majd T Mrayyan3, Kharabsheh Mohammad4
1Department of Community and Mental Health Nursing, Faculty of Nursing, The Hashemite University, Zarqa, Jordan. rula@hu.edu.jo.
BMC nursing
|May 15, 2025
概括
护士表现出高度的创造力,受诸如谦卑的领导力和心理安全等因素的影响. 一个机器学习模型有效地预测了创造力,帮助护士经理在决策和资源优化中发挥作用.
科学领域:
- 护理科学 护理科学
- 医疗保健中的人工智能
- 组织心理学 组织心理学
背景情况:
- 护士的创造力对于患者护理至关重要,但研究不足.
- 影响护士创造力的因素需要进一步调查.
- 开发护士创造力的预测模型是必不可少的.
研究的目的:
- 探索影响护士创造力的因素.
- 开发基于机器学习的决策支持系统 (DSS) 来预测护士的创造力.
- 为了提高对护士职业创造力的理解.
主要方法:
- 一种混合方法的方法,将机器学习与描述性,横截面,相关设计相结合.
- 调查数据来自约旦八家医院的191名注册护士.
- 纯粹的贝叶斯模型和多重线性回归模型被用于分析.
主要成果:
- 工作人员护士报告了高水平的创造力 (M=44.95).
- 机器学习模型对创造力预测因子 (例如,心理安全99%,性别98%) 的预测准确度很高.
- 谦卑的领导力,心理安全,经验,质量举措和教育水平是创造力的关键预测因素.
结论:
- 护士经理应该培养谦卑的领导力和心理安全,以促进创造力.
- 开发的DSS可以帮助评估护士的创造力,以更好地管理员工.
- 机器学习为预测和理解护士创造力提供了一个精确的工具.
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