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.

对全球人口健康的全面衡量为决策者提供了一个客观的基础，以确定健康优先事项，作出具有成本效益的资源分配决定，并评估干预措施的影响。然而，由于缺乏高质量的全球卫生数据，以及难以对福祉、成本和可能因地区而异的其他因素进行普遍衡量，因此，对全球卫生成果进行准确衡量是一项严峻的挑战。近年来，世界上最重要的全球流行病学研究-全球疾病负担研究-发起了全球合作努力，以加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构建跨文化背景的科学共识和共同理解。将传播这项研究的结果，以帮助研究人员和决策者更好地了解大规模国际数据合作如何作为达成共识、谈判全球社会目标和项目以及提高卫生措施的普遍性和准确性的场所。本研究运用人类学的方法，探讨全球性生物多样性研究中全球合作的社会和技术微观实践。研究人员将对GBD研究机构的研究人员进行参与性观察和访谈，该机构位于华盛顿西雅图的健康评估研究所以及他们的3,500名全球合作者。研究人员还将对GBD研究所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。除了培养社会科学研究方法的研究生外，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，帮助公众更好地理解全球健康中特定措施和数据技术的社会和科学意义和功能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。