Theresa A Koleck, Caitlin Dreisbach, Chen Zhang, Susan Grayson, Maichou Lor, Zhirui Deng, Alex Conway, Peter D R Higgins, Suzanne Bakken
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引用次数: 0
摘要
目标:将健康的社会决定因素纳入健康结果研究将使研究人员能够研究健康不平等问题。我们所有人研究计划有可能成为丰富的健康社会决定因素数据来源。然而,我们需要用户友好型的建议来对 "我们所有人的社会决定因素健康调查 "进行评分和解释,以便通过提高研究人员使用 "我们所有人的研究中心 "研究人员工作台的能力来为社区创造价值。我们创建了一份用户指南,旨在为研究人员提供健康状况社会决定因素调查的概述、对参与者回复进行评分和解释的建议,以及易于执行的 R 和 Python 函数:本用户指南的目标受众是 "我们所有人 "研究中心(All of Us Research Hub)研究人员工作台(Researcher Workbench)的注册用户,该工作台是一个支持 "我们所有人 "数据分析的云平台,目前正在使用或计划使用健康社会决定因素调查进行分析:我们介绍了作为健康社会决定因素调查一部分而评估的 14 个构造,并总结了构造的可操作性。我们提供了 30 项参考文献的建议,用于对参与者的回答进行评分和解释分数,其中 8 个构像有多个选项。然后,我们将通过 R 和 Python 函数示例来重新标注回答和结构式评分,这些函数可直接在研究者工作台的 Jupyter Notebook 或 RStudio 中实现。完整的源代码可在补充文件和 GitHub 中获取。最后,我们将讨论与研究人员健康社会决定因素调查相关的心理测量注意事项。
User guide for Social Determinants of Health Survey data in the All of Us Research Program.
Objectives: Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions.
Target audience: This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey.
Scope: We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.
期刊介绍:
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.