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基于机器学习的河北省部分地区神经重症从业人员心理健康现状调查

Research on mental health status of Hebei neurointensive care unit practitioners based on machine learning
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摘要 目的了解河北省部分地区神经重症从业人员心理健康现状,可能影响心理健康状况的相关因素,以便及时采取高效、综合的措施保护神经重症从业人员的心理健康。方法河北医科大学第二医院信息中心于2018年12月13日至12月14日在微信平台“问卷星”上进行横断面调查。本研究通过重症E学院(CCEI)和中国冷静治疗研究组(CNCSG),联系河北多家三甲医院的神经重症从业人员完成。调查包括22项指标,包括神经重症从业人员基本信息(婚姻、子女状况、个人收入等)、医院工作(平均每周工作时间、夜班频次、医院环境等),以及SCL-90的心理评估结果。结果使用一种机器学习方法:极度梯度提升树法(XGBoost),在中国神经重症从业人员问卷调查样本(n=354)中比较了16种影响心理健康风险因素。心理健康状况采用SCL-90问卷评估:阳性项目数≥43,筛查为阳性。在354人中有188人(53%)心理健康状况为阳性,用机器学习预测后得出受试者工作曲线曲线下面积为0.74。结论利用XGBoost法中特征重要性排序得出,夜班频率是最强的危险因素。说明夜班频率严重影响中国神经重症从业人员的心理健康,在排序中,年假长度、床位数、加床情况、夜班医生床位数比例、夜班护士床位数比例、总通勤时间占用比例均较高。医院可以制定适当的政策来解决这些因素,从而减少神经重症从业人员的心理压力,使其更好地服务患者。 Objective To examine the mental health status of medical staffs working in Neurointensive Care Unit(NICU)and explore its relevant factors.So that the mental health of NICU medical staffs may be protected with comprehensive and effective approaches.Methods This cross-sectional study was carried out from December13 to December 14,2018,by the"Question Star"WeChat platform.Chinese ICU medical staffs from 3 hospitals in 34 provinces of China were recruited for this study through the Critical Care E Institute(CCEI)and the Chinese Neurocritical Care Study Group(CNCSG).Twenty-two indicators were included in this survey,including fundamental details about the ICU medical staffs(marriage status,child-rearing situation,income,etc.),hospital work(weekly working hours,night shifts,hospital environment,etc.)and the symptom checklist-90(SCL-90)were used to evaluate psychological symptoms.Results A questionnaire administered to Chinese NICU medical staffs(n=354)evaluated16 risk variables impacting mental health.Machine learning method with Extreme Gradient Boosting(XGBoost)was used to investigate these risk factors.The results of SCL-90 showed that 43 out of 90 items were positive.The accuracy rate and area under the AUC curve after utilizing machine learning to predict turnover intention were 53% and 0.74,respectively.The biggest risk factor,according to the ranking of feature relevance in XGBoost,was the frequency of night shifts.It showed that the frequency of night shifts strongly affected the mental health of Chinese NICU staffs.Additionally,a significant portion of the ranking was based on the length of annual leave,number of beds,availability of extra beds,proportion of doctor and night nurse beds,and the total commute time.Conclusion There are obvious problems in the mental health status of Chinese neurointensive care unit practitioners.In order to reduce the psychological pressure of NICU staffs,appropriate rules should be developed by hospitals.
作者 贺峰 邢丽娜 任雪璞 李文芳 胡耀群 赵迪 He Feng;Xing Linan;Ren Xuepu;Li Wenfang;Hu Yaoqun;Zhao Di(Information Center,the Second Hospital of Hebei Medical University,Shijiazhuang 050000,China)
出处 《脑与神经疾病杂志》 CAS 2024年第4期240-246,共7页 Journal of Brain and Nervous Diseases
基金 河北省医学科学研究计划课题(20241135)。
关键词 重症医学 极度梯度提升树法 可视化 问卷调查 症状自评量表-90 心理健康 Critical care medicine Extreme gradient boosting Visualization Questionnaire survey Symptom checklist-90 Mental health
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