Suppr超能文献

利用基于模糊自然语言的关联规则挖掘评估新冠疫情期间医护人员与工作相关的职业压力和焦虑

Assessing Occupational Work-Related Stress and Anxiety of Healthcare Staff During COVID-19 Using Fuzzy Natural Language-Based Association Rule Mining.

作者信息

Alkabaa Abdulaziz S, Taylan Osman, Alqabbaa Hanan S, Guloglu Bulent

机构信息

Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Department of Management Engineering, Faculty of Management, Istanbul Technical University, 34367 Istanbul, Türkiye.

出版信息

Healthcare (Basel). 2025 Jul 18;13(14):1745. doi: 10.3390/healthcare13141745.

Abstract

Frontline healthcare staff who contend diseases and mitigate their transmission were repeatedly exposed to high-risk conditions during the COVID-19 pandemic. They were at risk of mental health issues, in particular, psychological stress, depression, anxiety, financial stress, and/or burnout. This study aimed to investigate and evaluate the occupational stress of medical doctors, nurses, pharmacists, physiotherapists, and other hospital support crew during the COVID-19 pandemic in Saudi Arabia. We collected both qualitative and quantitative data from a survey given to public and private hospitals using methods like correspondence analysis, cluster analysis, and structural equation models to investigate the work-related stress (WRS) and anxiety of the staff. Since health-related factors are unclear and uncertain, a fuzzy association rule mining (FARM) method was created to address these problems and find out the levels of work-related stress (WRS) and anxiety. The statistical results and K-means clustering method were used to find the best number of fuzzy rules and the level of fuzziness in clusters to create the FARM approach and to predict the work-related stress and anxiety of healthcare staff. This innovative approach allows for a more nuanced appraisal of the factors contributing to work-related stress and anxiety, ultimately enabling healthcare organizations to implement targeted interventions. By leveraging these insights, management can foster a healthier work environment that supports staff well-being and enhances overall productivity. This study also aimed to identify the relevant health factors that are the root causes of work-related stress and anxiety to facilitate better preparation and motivation of the staff for reorganizing resources and equipment. The results and findings show that when the financial burden (FIN) of healthcare staff increased, WRS and anxiety increased. Similarly, a rise in psychological stress caused an increase in WRS and anxiety. The psychological impact (PCG) ratio and financial impact (FIN) were the most influential factors for the staff's anxiety. The FARM results and findings revealed that improving the financial situation of healthcare staff alone was not sufficient during the COVID-19 pandemic. This study found that while the impact of PCG was significant, its combined effect with FIN was more influential on staff's work-related stress and anxiety. This difference was due to the mutual effects of PCG and FIN on the staff's motivation. The findings will help healthcare managers make decisions to reduce or eliminate the WRS and anxiety experienced by healthcare staff in the future.

摘要

在新冠疫情期间,奋战在一线对抗疾病并减缓其传播的医护人员反复面临高风险状况。他们有出现心理健康问题的风险,尤其是心理压力、抑郁、焦虑、经济压力和/或职业倦怠。本研究旨在调查和评估沙特阿拉伯在新冠疫情期间医生、护士、药剂师、物理治疗师及其他医院辅助人员的职业压力。我们通过对应分析、聚类分析和结构方程模型等方法,从对公立和私立医院进行的一项调查中收集定性和定量数据,以调查工作人员的工作相关压力(WRS)和焦虑情况。由于与健康相关的因素不明确且不确定,因此创建了一种模糊关联规则挖掘(FARM)方法来解决这些问题,并找出工作相关压力(WRS)和焦虑的程度。利用统计结果和K均值聚类方法来找出最佳的模糊规则数量和聚类中的模糊程度,以创建FARM方法,并预测医护人员的工作相关压力和焦虑。这种创新方法能够更细致入微地评估导致工作相关压力和焦虑的因素,最终使医疗机构能够实施有针对性的干预措施。通过利用这些见解,管理层可以营造一个更健康的工作环境,支持员工的福祉并提高整体生产力。本研究还旨在确定作为工作相关压力和焦虑根源的相关健康因素,以便更好地为员工重新组织资源和设备做好准备并激发其积极性。结果表明,当医护人员的经济负担(FIN)增加时,工作相关压力(WRS)和焦虑也会增加。同样,心理压力的增加也会导致工作相关压力(WRS)和焦虑的增加。心理影响(PCG)比率和经济影响(FIN)是影响员工焦虑的最主要因素。FARM的结果表明,在新冠疫情期间,仅改善医护人员的经济状况是不够的。本研究发现,虽然心理影响(PCG)的影响很大,但其与经济影响(FIN)的综合作用对员工的工作相关压力和焦虑更具影响力。这种差异是由于心理影响(PCG)和经济影响(FIN)对员工积极性的相互作用所致。这些研究结果将有助于医疗保健管理人员做出决策,以减少或消除未来医护人员所经历的工作相关压力和焦虑。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验