论文标题

中国生物医学研究人员的心血管风险和工作压力:一种观察,大数据研究方案

Cardiovascular risk and work stress in biomedical researchers in China: An observational, big data study protocol

论文作者

Zhu, Fang, Zhang, Qian, Chen, Hao, Shi, Guocheng, Wen, Chen, Zhu, Zhongqun, Chen, Huiwen

论文摘要

简介:互联网技术可以加强数据收集和集成,并已在公共卫生研究中广泛使用。有必要应用这项技术进一步研究生物医学研究人员的行为和健康。研究人员和临床医生开发了基于浏览器的扩展,以促进研究人员行为和心理数据的收集和分析。该协议说明了一项旨在(1)表征中国生物医学研究人员健康状况并评估工作压力,工作满意度,角色冲突,角色歧义和家庭支持的观察性研究; (2)确定工作,行为和健康之间的关联; (3)调查行为与心理状况之间的关联。我们的发现将有助于理解工作,工作环境和家庭支持对生物医学研究人员身心健康的影响。方法和分析:这是一项前瞻性观察研究;所有候选人将从中国招募。参与者将在其Internet浏览器上安装扩展名,该扩展程序将在访问PubMed时收集数据。基于网络的调查将每6个月发送到用户界面,其中涉及社会人口统计学变量,感知的压力量表,工作满意度量表,角色冲突和歧义性量表以及家庭支持量表。机器学习算法将分析每日访问期间生成的数据。道德与传播:这项研究获得了上海儿童医疗中心伦理委员会(参考号SCMCIRB-K2018082)的伦理批准。研究结果将通过同行评审的出版物和会议演讲来传播。

Introduction: Internet technologies could strengthen data collection and integration and have been used extensively in public health research. It is necessary to apply this technology to further investigate the behaviour and health of biomedical researchers. A browser-based extension was developed by researchers and clinicians to promote the collection and analysis of researchers' behavioural and psychological data. This protocol illustrates an observational study aimed at (1) characterising the health status of biomedical researchers in China and assessing work stress, job satisfaction, role conflict, role ambiguity, and family support; (2) identifying the association between work, behaviour, and health; and (3) investigating the association between behaviour and mental status. Our findings will contribute to the understanding of the influences of job, work environment, and family support on the mental and physical health of biomedical researchers. Methods and analysis: This is a prospective observational study; all candidates will be recruited from China. Participants will install an extension on their Internet browsers, which will collect data when they are accessing PubMed. A web-based survey will be sent to the user interfaces every 6 months that will involve sociodemographic variables, perceived stress scale, job satisfaction scale, role conflict and ambiguity scale, and family support scale. Machine-learning algorithms will analyse the data generated during daily access. Ethics and dissemination: This study received ethical approval from the ethics committee of the Shanghai Children's Medical Centre (reference number SCMCIRB-K2018082). Study results will be disseminated through peer-reviewed publications and conference presentations.

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