Community-engaged research partnerships for building capacity and training in inclusive data science

社区参与的研究伙伴关系，以建设包容性数据科学的能力和培训

• 批准号: 10665379
• 负责人: Lisa K Marriott
• 金额: $ 27.19万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-17 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665379
• 关键词: Acceleration; Address; Award; Biomedical Research; Career Choice; Collection; Communities; Data; Data Collection; Data Protection; Data Reporting; Data Science; Data Security; Decision Making; Development; Education; Educational process of instructing; Equity; Ethics; Evaluation; Exclusion; Feedback; Health; High School Student; Individual; Inequity; Informatics; Institution; K-12 student; Learning; Mathematics; Measures; Middle School Student; Outcome; Output; Preparation; Prevalence; Privacy; Qualitative Methods; Qualitative Research; Recommendation; Reporting; Research; Research Ethics; Research Personnel; Research Training; STEM program; Science; Science, Technology, Engineering and Mathematics; Science, Technology, Engineering and Mathematics Education; Strategic Planning; Students; Surface; Teaching Method; Technology; Training; Training Programs; Underrepresented Populations; Underrepresented Students; United States National Institutes of Health; Voice; Work; career; community engaged research; data dictionary; data integration; data integrity; data visualization; demographics; evidence base; experience; hands-on learning; improved; informatics tool; innovation; intersectionality; junior high school; marginalization; multimodality; novel strategies; peer coaching; policy recommendation; programs; recruit; resource guides; skills; student participation; tool

PROJECT SUMMARY Increasing diversity within STEM (science, technology, engineering, and mathematics) and the biomedical workforce is prioritized by federal strategic plans. Federal efforts aimed at advancing equity call for improvements in demographic data practices. While essential, our prior work documented that research training programs for K-12 students remain challenged to accurately evaluate diversity and have requested guidance around implementation and expanded reporting. Our prior work with equity-focused researchers and STEM partners defined a set of expanded demographics that can more inclusively measure individual identities. These demographics are currently being integrated into our previously developed informatics platforms that deliver tailored research and education about health (Let’s Get Healthy!) and STEM development (STEM Assessment and Reporting Tracker; START). The proposed project leverages data visualization outputs from these informatics tools to surface conversations about demographic data collection and responsible reporting with our cross-institutional partners representing STEM education, research, and diverse communities. Inclusive demographics permit the identification of individuals who are being excluded, marginalized, or improperly aggregated, thereby increasing capacity to address inequities in STEM and biomedical research training. Together, this collaborative project aims to 1) identify considerations for inclusive demographic data collection and responsible reporting; 2) establish a training collaborative that aligns qualitative research with data science outputs to enhance authentic representation of a diverse biomedical workforce; and 3) characterize how demographic data are used to make decisions and inform practice. This project aligns interprofessional partners at roundtables to inform demographic data practices for use by STEM programs. It offers multi-modal training of middle and high school students in research experiences that align data science, qualitative research, and applied ethics for enhancing accurate and authentic representation of identities. Finally, it builds the capacity of K-12 students and programs in inclusive data science, which become transferrable research skills as students continue their careers. Our project enables STEM programs to estimate prevalence and understand program impact for underrepresented populations, ultimately making STEM evaluation easier for programs as we collectively work to improve STEM engagement and persistence for diverse student groups. As trainees do not enter training programs with equal access, accommodations, or preparation, inclusive demographic measures can inform a nuanced set of program outcomes, facilitating research on intersectionality between demographic identities and supporting the recruitment and retention of historically underrepresented students in biomedical research.

项目总结 增加STEM(科学、技术、工程和数学)和生物医学领域的多样性 劳动力是联邦战略计划的优先事项。旨在推进公平的联邦努力呼吁 改进人口数据做法。虽然很重要，但我们之前的工作记录了这项研究 针对K-12学生的培训计划在准确评估多样性方面仍然面临挑战，并已要求 关于实施和扩大报告的指导。我们之前与专注于股票的研究人员和 STEM合作伙伴定义了一组扩展的人口统计数据，可以更全面地衡量个人 身份。这些人口统计数据目前正被整合到我们以前开发的信息学中 提供量身定制的健康研究和教育的平台(让我们变得健康！)和STEM 发展(STEM评估和报告跟踪；START)。提议的项目利用数据 这些信息学工具的可视化输出，以使有关人口统计数据收集的对话浮出水面 与代表STEM教育、研究和培训的跨机构合作伙伴一起提供负责任的报告 多样化的社区。包容性人口统计允许识别被排除在外的个人， 被边缘化，或不适当地聚合，从而提高解决STEM不平等问题的能力 生物医学研究培训。总之，该协作项目旨在1)确定包容性的考虑因素 人口统计数据收集和负责任的报告；2)建立培训协作，使 利用数据科学成果进行定性研究，以增强多样化生物医学的真实代表性 劳动力；以及3)描述人口统计数据如何用于决策和为实践提供信息。这 项目在圆桌会议上协调专业间合作伙伴，为STEM使用的人口统计数据实践提供信息 程序。它为初中生提供多模式的培训，使他们的研究经验与 数据科学、定性研究和应用伦理学，以增强准确和真实的 身份。最后，它建立了K-12学生和项目在包容性数据科学方面的能力，这些能力成为 随着学生继续他们的职业生涯，可转让的研究技能。我们的项目使STEM项目能够 估计流行率并了解计划对代表性不足人群的影响，最终使 随着我们共同努力提高STEM参与度和持久性，计划的STEM评估变得更容易 针对不同的学生群体。由于受训人员没有平等的机会、住宿或 准备，包容性人口统计措施可以提供一套细致入微的方案成果，促进 人口特征与支持招聘和留住人才之间的交叉研究 在生物医学研究中，学生的代表性历来偏低。