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.

全球人口健康的综合衡量标准为决策者制定卫生优先事项、做出具有成本效益的资源分配决策以及评估干预措施的影响提供了客观基础。然而，由于缺乏高质量的全球卫生数据，而且难以制定针对福祉、成本和其他可能因地区而异的因素的普遍衡量标准，因此对全球健康结果进行准确衡量非常具有挑战性。近年来，世界上最重要的全球流行病学研究——全球疾病负担（GBD）研究启动了全球合作努力，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合作努力的具体微观实践，以了解全球专家如何利用数字和数据技术在不同文化背景下达成科学共识和共同理解。这项研究的结果将以这样的方式传播，以帮助研究人员和政策制定者更好地了解大规模国际数据合作如何作为形成共识、谈判全球社会目标和项目以及提高普遍性和准确性的场所的卫生措施。本研究采用人类学方法来调查 GBD 研究的全球合作的社会和技术微观实践。研究人员将对 GBD 研究所在地华盛顿州西雅图健康指标与评估研究所的研究人员及其 3,500 名全球合作者进行参与观察和访谈。研究人员还将对 GBD 研究使用的量化程序进行历史和符号学分析，以评估专家在生成和审查健康数据时如何平衡科学、伦理和社会考虑因素和目标。除了培训研究生社会科学研究方法外，这项研究还将有助于跨学科努力，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。研究成果将用于制作教育材料，帮助公众更好地了解特定措施和数据技术在全球健康中的社会和科学意义及功能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。