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

基于模型的行为检查

• 批准号: 9235737
• 负责人: Joshua Hawley
• 金额: $ 10.44万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9235737
• 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.