基于IPOD模型,识别基于学士学位毕业前后护理学和退学的预测因素:一种机器学习方法
Mahdieh Arian1, Azadeh Kamali2, Zahra Dalir1
1Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran; Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Nurse education in practice
|October 20, 2025
概括
预测护理学生的学和专业的磨损是至关重要的. 早期的学业成绩和以后的工作倦怠显著影响护理职业的保留.
科学领域:
- 护理教育研究 护理教育研究
- 医疗保健劳动力研究 劳动力研究
- 在学术界的预测分析.
背景情况:
- 在护理学生的留学和专业的磨损全球挑战.
- 需要有效的策略来提高护理工作人员的稳定性.
研究的目的:
- 确定在毕业前学业学的预测因素.
- 确定毕业后职业消耗的预测因素.
- 在护理教育中利用机器学习进行预测建模.
主要方法:
- 对878名本科护理学生 (2007-2018) 的回顾性研究.
- 通过机构记录和后续访谈收集数据.
- 机器学习模型 (XGBoost,SVM,DT,mLR,RF,BLR) 的应用用于预测.
主要成果:
- 学业学率: 2.2%; 职业退学率: 28.3% .
- 关键的学业学预测因素:学期失败,早期的GPA低,年龄较小,学费支付,男性性别.
- 主要的职业消耗预测因素:高临床能力,高GPA,就业延迟,工作倦怠,工作经验.
结论:
- 早期的学术指标预测护理学生学.
- 职业因素,如工作倦怠和就业延迟,预测毕业后的消耗.
- 研究结果支持针对性的干预措施,以改善护理人员的留守率.
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