A Model-Based Examination of Behavioral & Social Science Workforce: Improving Health Outcomes

基于模型的行为检查

• 批准号: 8998961
• 负责人: Joshua Hawley
• 金额: $ 38.39万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8998961
• 关键词: Accounting; Advisory Committees; Assimilations; Award; Basic Science; Behavioral; Belief; Biological Models; Biological Sciences; Biomedical Research; Collaborations; Committee Members; Data; Data Analyses; Economic Factors; Economics; Faculty; Funding; Future; Goals; Government; Health; Healthcare; Individual; Institution; Institutional Policy; Knowledge; Maintenance; Mission; Modeling; Outcome; Policies; Population Heterogeneity; Positioning Attribute; Production; Productivity; Public Policy; Publishing; Qualifying; Reporting; Research; Research Personnel; Research Support; Retirement; Scholarship; Science; Source; Structure; Students; Surveys; System; United States National Institutes of Health; Universities; Work; base; behavioral/social science; design; dynamic system; experience; faculty research; health economics; improved; improved outcome; mathematical model; member; model building; programs; public health relevance; research and development; simulation; social; tool

 DESCRIPTION (provided by applicant): Science Workforce Analysis and Modeling: Understanding the Contributions of Social and Behavioral Sciences to Health Outcomes. The National Institutes of Health (NIH) recognizes that a robust science workforce is required to achieve national research and development goals. This renewal application develops and applies systems modeling frameworks to the science workforce, focusing on the portion of the social and behavioral workforce working to improve health outcomes. The goal is to identify policy levers to improve the science workforce for NIH and to provide practical guidance to decision makers tasked with improving the staffing of our nation's health maintenance and health care workforce. The research will use systems modeling to understand the relationships between individuals, institutions and policies in the science workforce. The research will employ combination of systems approaches, mathematical models, and data analyses. Data will be drawn from many sources including federal panel data such as the Survey of Earned Doctorates, institutional data from universities and federal agencies, and publically available data. In collaboration with NIH staff, and the advisory board for the Science Workforce Analysis and Modeling (SWAM), our research team plans to conduct research into key questions that are important to NIH's ability to understand how to assemble the science workforce for tomorrow. Researchers will first analyze the number of faculty positions in social and behavioral science fields to better understand the creation of new opportunities for researchers. Second, the research team will assess the impact of NIH funding levels on the research awards, productivity of the research, and impact on science workforce. Third, using data from individual institutions, research will examine the production of new graduates in social and behavioral science fields and their subsequent assimilation into the contributing science workforce. Finally, most likely using agent-based modeling, the researchers will design and build a dynamic model of the science workforce system, helping to understand how individual choices and institutional policies interact with each other to produce the most qualified workforce for the future of the nation. Whenever possible, the analyses at all levels will consider issues of workforce diversity and associated policy levels to improve outcomes in merit-based settings. Through models, leaders at NIH and the government can understand programs designed to improve the workforce, as well as describe the tradeoffs and unanticipated results of policies. Models of the science workforce must take into account the fact that the workforce of tomorrow depends both on changes to inputs that NIH has some control over (such as research funding) and social or economic realities that agencies can not readily influence.

 描述(由申请者提供)：科学劳动力分析和建模：了解社会科学和行为科学对健康结果的贡献。美国国立卫生研究院(NIH)认识到，要实现国家研究和发展目标，需要一支强大的科学队伍。这一更新应用程序开发了系统建模框架，并将其应用于科学工作人员，重点关注致力于改善健康结果的社会和行为工作人员部分。其目标是确定政策杠杆，以改善NIH的科学队伍，并为负责改善我国卫生维护和卫生保健队伍人员配置的决策者提供实用指导。这项研究将使用系统建模来理解科学工作者中个人、机构和政策之间的关系。研究将采用系统方法、数学模型和数据分析相结合的方法。数据将来自许多来源，包括联邦小组数据，如博士生调查，大学和联邦机构的机构数据，以及公开可用的数据。我们的研究团队计划与NIH工作人员以及科学劳动力分析和建模(SWAM)顾问委员会合作，对NIH了解如何为未来组建科学劳动力的能力至关重要的关键问题进行研究。研究人员将首先分析社会和行为科学领域的教师职位数量，以更好地了解为研究人员创造新机会的情况。其次，研究团队将评估NIH资金水平对研究奖项、研究生产率和对科学劳动力的影响。第三，研究将使用来自各个机构的数据，检查社会和行为科学领域的应届毕业生的产出，以及他们随后被贡献的科学劳动力所同化的情况。最后，研究人员最有可能使用基于代理的建模，设计和构建科学劳动力系统的动态模型，帮助理解个人选择和制度政策如何相互作用，为国家的未来培养最合格的劳动力。在可能的情况下，各级的分析将考虑劳动力多样性和相关的政策层面的问题，以改进择优环境下的结果。通过模型，NIH的领导人和政府可以了解旨在改善劳动力的计划，以及描述政策的权衡和意想不到的结果。科学劳动力的模型必须考虑到这样一个事实，即未来的劳动力既取决于NIH可以控制的投入的变化(例如研究资金)，也取决于机构不能轻易影响的社会或经济现实。