Doctoral Dissertation Research: The Role of New Data Technologies in the Collaborative Development of Accurate Health Measures

博士论文研究：新数据技术在协同开发准确健康措施中的作用

• 批准号: 1917914
• 负责人: Arthur Kleinman
• 金额: $ 2.52万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917914&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Role; New

Comprehensive measures of global population health provide decision-makers with an objective basis for setting health priorities, making cost-effective resource allocation decisions, and assessing the impact of interventions. However, producing accurate measures of global health outcomes is acutely challenging, due to both the lack of quality global health data, and the difficulty of producing universal measures for well-being, cost, and other factors that may vary by region. In recent years, the world's foremost global epidemiology study, the Global Burden of Disease (GBD) Study, has initiated global collaborative efforts to bolster the objectivity and universality of their data and measures. The research supported by this award investigates the grounded micro-practices of these global collaborative efforts in order to understand how global experts use numbers and data technologies to craft scientific consensus and common understandings across cultural contexts. The findings of this research will be disseminated in such a way to aid researchers and policy-makers better understand how large-scale international data collaborations may operate as venues for forging common understandings, negotiating global social goals and projects, and improving the universality and accuracy of health measures. This research uses anthropological methods to investigate the social and technical micro-practices of global collaboration for the GBD study. The researcher will conduct participant observation and interviews with researchers at the institutional home of the GBD study, the Institute for Health Metrics and Evaluation in Seattle, Washington, as well as with their 3,500 global collaborators . The researcher will also conduct historical and semiotic analyses of the quantification procedures used by the GBD study over time in order to evaluate how experts balance scientific, ethical, and social considerations and goals while producing and reviewing health data. In addition to training a graduate student in the methods of social science research, this research will contribute to interdisciplinary efforts to understand the social dynamics of global scientific collaboration and the various ways that social goals and values influence the design and measurement of global health data. Research results will be used to create education materials that will help the public better understand the social and scientific significance and functions of specific measures and data technologies in global health.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

全球人口健康的全面衡量标准为决策者提供了确定健康优先事项，做出成本效益的资源分配决策以及评估干预措施的客观基础。但是，由于缺乏优质的全球健康数据，以及为福祉，成本和其他可能因地区而有所不同的因素而难以产生通用措施，因此对全球健康成果进行准确的衡量标准是巨大的挑战。近年来，全球最重要的全球流行病学研究，《全球疾病负担》（GBD）研究，已经开始了全球合作努力，以增强其数据和措施的客观性和普遍性。该奖项支持的研究调查了这些全球协作努力的基础微观实践，以了解全球专家如何使用数字和数据技术来制定科学共识和跨文化背景的共同理解。这项研究的发现将通过这种方式传播，以帮助研究人员和政策制定者更好地了解大规模的国际数据合作可能是如何作为建立共同理解，谈判全球社会目标和项目以及提高健康措施的普遍性和准确性的场所。这项研究使用人类学方法来研究GBD研究全球合作的社会和技术微观实践。研究人员将在GBD研究，华盛顿西雅图的卫生指标与评估研究所以及其3500名全球合作者的机构住所进行参与者的观察和访谈。研究人员还将对GBD研究随着时间的推移使用的量化程序进行历史和符号学分析，以评估专家在制作和审查健康数据时如何平衡科学，道德和社会考虑和目标。除了培训研究生的社会科学研究方法外，这项研究还将有助于跨学科的努力，以了解全球科学合作的社会动态以及社会目标和价值观影响全球健康数据的设计和测量的各种方式。研究结果将用于创建教育材料，以帮助公众更好地了解全球健康中特定措施和数据技术的社会和科学意义以及功能。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。