Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Cross-sectional design, self-reporting screening scales.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Cross-sectional design, self-reporting screening scales.
CONCLUSIONS: 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.
摘要：
背景：职业压力和工作环境适应性对医生和护士之间心理健康差异的影响尚不清楚。本研究旨在确定和排名的关键决定因素的医生和护士在中国，并比较他们对心理健康的影响在医生和护士之间的差异。
方法：进行了多阶段整群抽样的大型横断面调查。调查包括焦虑自评量表(SAS量表),流行病学研究中心抑郁量表(CES-D量表)，Maslach职业倦怠综合调查(MBI-GS)和人-环境(PE)拟合。我们应用了一个有原则的，基于机器学习的变量选择算法，使用随机森林，确定医生和护士心理健康的决定因素并对其进行排名。
结果：在我们的研究中，我们分析了9964名医护人员的样本，和2729（27％）是医生。医生和护士的焦虑和抑郁障碍患病率分别为31.0％和53.3％，30.8%和47.9%，分别。在患有焦虑症的医生中，我们观察到愤世嫉俗的可能性更高，情绪疲惫,个人成就感降低,组织适应性差，工作适应性，团体健身，和主管健身，按重要性排序。当比较医生对抑郁症的影响时，团体健身和主管健身没有显著影响.对护士来说,与玩世不恭相比,情绪衰竭对抑郁障碍的影响更为显著.主管健身对护士的焦虑障碍没有显著影响。
结论：横截面设计，自我报告筛查量表。
结论：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相容性。此外,医生和护士中抑郁和焦虑障碍的关键决定因素略有差异。事实证明，采用机器学习方法有助于在中国的医生和护士中识别精神健康障碍的决定因素。这些发现可能有助于改善旨在解决医疗保健专业人员心理健康的政策制定。
方法：进行了多阶段整群抽样的大型横断面调查。调查包括焦虑自评量表(SAS量表),流行病学研究中心抑郁量表(CES-D量表)，Maslach职业倦怠综合调查(MBI-GS)和人-环境(PE)拟合。我们应用了一个有原则的，基于机器学习的变量选择算法，使用随机森林，确定医生和护士心理健康的决定因素并对其进行排名。
结果：在我们的研究中，我们分析了9964名医护人员的样本，和2729（27％）是医生。医生和护士的焦虑和抑郁障碍患病率分别为31.0％和53.3％，30.8%和47.9%，分别。在患有焦虑症的医生中，我们观察到愤世嫉俗的可能性更高，情绪疲惫,个人成就感降低,组织适应性差，工作适应性，团体健身，和主管健身，按重要性排序。当比较医生对抑郁症的影响时，团体健身和主管健身没有显著影响.对护士来说,与玩世不恭相比,情绪衰竭对抑郁障碍的影响更为显著.主管健身对护士的焦虑障碍没有显著影响。
结论：横截面设计，自我报告筛查量表。
结论：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相容性。此外,医生和护士中抑郁和焦虑障碍的关键决定因素略有差异。事实证明，采用机器学习方法有助于在中国的医生和护士中识别精神健康障碍的决定因素。这些发现可能有助于改善旨在解决医疗保健专业人员心理健康的政策制定。
