Lu Mengjie, Li Xiyang, Song Keyu, Xiao Yuyin, Zeng Wu, Shi Chenshu, Fan Xianqun, Li Guohong
School of Public Health, Shanghai Jiao Tong University School of Medicine, 227 South Chongqing Rd., Shanghai 200025, China.
Department of International Health, Georgetown University, 3700 Reservoir Rd. NW, Washington, DC 20057, United States of America.
J Affect Disord. 2024 Apr 1;350:350-358. doi: 10.1016/j.jad.2024.01.113. Epub 2024 Jan 12.
The impact of occupational stress and work environment fitness on mental health disparities between physicians and nurses are not well understood. This study aims to identify and rank key determinants of mental health in physicians and nurses in China and compare the differences in their impact on mental health between physicians and nurses.
A large cross-sectional survey with multistage cluster sampling was conducted. The survey included the Self-Rating Anxiety Scale (SAS Scale), the Center for Epidemiologic Studies Depression Scale (CES-D Scale), the Maslach Burnout Inventory-General Survey (MBI-GS) and the Person-Environment (PE) Fit. We applied a principled, machine learning-based variable selection algorithm, using random forests, to identify and rank the determinants of the mental health in physicians and nurses.
In our study, we analyzed a sample of 9964 healthcare workers, and 2729 (27 %) were physicians. The prevalence of anxiety and depressive disorders among physicians and nurses was 31.0 % and 53.3 %, 30.8 % and 47.9 %, respectively. Among physicians with anxiety disorder, we observed a higher likelihood of cynicism, emotional exhaustion, reduced personal accomplishment, and poor organization fitness, job fitness, group fitness, and supervisor fitness, in order of importance. When comparing the effects on depressive disorder in physicians, group fitness and supervisor fitness did not have significant impacts. For nurses, emotional exhaustion had a more significant effect on depressive disorder compared to cynicism. Supervisor fitness did not have a significant impact on anxiety disorder in nurses.
Cross-sectional design, self-reporting screening scales.
Compared to individual and hospital characteristics, the primary factors influencing mental health disorders are occupational burnout and the compatibility of the work environment. Additionally, the key determinants of depressive and anxiety disorders among doctors and nurses exhibit slight variations. Employing machine learning methods proves beneficial for identifying determinants of mental health disorders among physicians and nurses in China. These findings could help improve policymaking aimed at addressing the mental well-being of healthcare professionals.
职业压力和工作环境适应性对医生和护士心理健康差异的影响尚未得到充分理解。本研究旨在识别并排序中国医生和护士心理健康的关键决定因素，并比较它们对医生和护士心理健康影响的差异。
采用多阶段整群抽样进行大规模横断面调查。该调查包括自评焦虑量表（SAS量表）、流行病学研究中心抑郁量表（CES-D量表）、马氏职业倦怠通用问卷（MBI-GS）和人-环境（PE）适配度。我们应用一种基于机器学习的原则性变量选择算法，即随机森林，来识别并排序医生和护士心理健康的决定因素。
在我们的研究中，我们分析了9964名医护人员的样本，其中2729名（27%）是医生。医生和护士中焦虑症和抑郁症的患病率分别为31.0%和53.3%、30.8%和47.9%。在患有焦虑症的医生中，我们观察到玩世不恭、情绪耗竭、个人成就感降低以及组织适应性、工作适应性、团队适应性和上级适应性差的可能性更高，按重要性排序。在比较对医生抑郁症的影响时，团队适应性和上级适应性没有显著影响。对于护士来说，与玩世不恭相比，情绪耗竭对抑郁症的影响更大。上级适应性对护士的焦虑症没有显著影响。
横断面设计、自我报告筛查量表。
与个人和医院特征相比，影响心理健康障碍的主要因素是职业倦怠和工作环境的兼容性。此外，医生和护士中抑郁和焦虑障碍的关键决定因素存在细微差异。采用机器学习方法有助于识别中国医生和护士心理健康障碍的决定因素。这些发现有助于改进旨在解决医护人员心理健康问题的政策制定。
