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（应用程序提供）：科学劳动力分析和建模：了解社会和行为科学对健康成果的贡献。美国国立卫生研究院（NIH）认识到，实现国家研发目标需要强大的科学劳动力。这种更新的应用程序开发并将系统建模框架应用于科学劳动力，重点关注社会和行为劳动力的部分，以改善健康成果。目的是确定政策杠杆，以改善NIH的科学劳动力，并为负责改善国家健康维护和医疗保健员工人员配备的决策者提供实践指导。该研究将使用系统建模来了解科学劳动力中个人，机构和政策之间的关系。该研究将采用系统方法，数学模型和数据分析的组合。数据将来自许多来源，包括联邦小组数据，例如对获得博士学位的调查，大学和联邦机构的机构数据以及公开可用的数据。与NIH员工和科学劳动力分析和建模咨询委员会（SWAM）合作，我们的研究团队计划对关键问题进行研究，这对于NIH了解如何为明天的明天组装科学劳动力很重要。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。其次，研究小组将评估NIH资金水平对研究奖，研究生产力以及对科学劳动力的影响的影响。第三，使用来自各个机构的数据，研究将研究社会和行为科学领域的新毕业生的生产，以及随后对贡献科学劳动力的同化。最后，研究人员最有可能使用基于代理的建模，设计并建立科学劳动力系统的动态模型，以帮助了解个人选择和机构政策如何相互互动，从而为国家未来提供最合格的劳动力。只要有可能，各级分析将考虑劳动力多样性和相关政策水平的问题，以改善基于绩效的设置的结果。通过模型，NIH和政府的领导人可以理解旨在改善劳动力的计划，并描述政策的权衡和意外的结果。科学劳动力的模型必须考虑到明天的劳动力既取决于NIH对投入的变化（例如研究资金）以及机构无法轻易影响的社会或经济现实的变化。