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瘤护士的倦怠保护模式:使用机器学习分析的横截面研究.

Ana Rocha1,2, Cristina Costeira3,4,5, Raul Barbosa6

  • 1Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), Coimbra, 3004-011, Portugal. anamnrocha@esenfc.pt.

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具有永久合同,工作与生活平衡,支持性环境的瘤护士表现出减少了燃烧. 管理角色和家长等保护因素也在缓解倦怠方面发挥着作用.

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燃烧的疲劳是一种疲劳.机器学习是机器学习.职业健康 职业健康 职业健康 职业健康瘤学护理 癌症护理保护因素 保护因素工作环境 工作环境

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科学领域:

  • 护理 护理 护理
  • 在瘤学瘤学.
  • 心理学 心理学 心理学

背景情况:

  • 瘤护士面临着对患有危及生命的疾病的患者进行护理的强烈要求.
  • 在这个人群中,职业倦怠是一个重大的问题.
  • 识别保护因素和风险因素对于缓解至关重要.

研究的目的:

  • 为了确定瘤护士的倦怠概况.
  • 确定社会 - 人口和与工作有关的保护模式,防止倦怠.

主要方法:

  • 葡萄牙150名瘤护士的横截面研究.
  • 使用了Maslach Burnout Inventory (MBI) 和自我管理的问卷.
  • 采用KMeans集群和随机森林机器学习算法.

主要成果:

  • 确定了六种保护模式,包括永久合同,工作与生活平衡以及支持性的工作环境.
  • 管理角色和家长 (两个或更多的孩子) 显示出潜在的保护作用.
  • 机器学习强调了倦怠的不可预测性和保护因素的重要性.

结论:

  • 建立弹性战略和保护因素 (工作稳定,经验,休息) 对于减少瘤护士倦怠至关重要.
  • 研究结果表明,需要有针对性的,特定于环境的倦怠预防计划.
  • 建议进行进一步的以假设为导向的研究以验证.