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; 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.

项目摘要 茎（科学，技术，工程和数学）和生物医学的多样性增加 联邦战略计划优先考虑劳动力。旨在提高公平呼吁的联邦努力 人口数据实践的改进。尽管必不可少，但我们先前的工作记录了研究 针对K-12学生的培训计划仍然受到挑战，无法准确评估多样性并要求 有关实施和扩展报告的指导。我们先前与以股权为重点的研究人员和 STEM合作伙伴定义了一组扩展的人口统计信息，可以更全面地衡量个人 身份。这些人群目前正在整合到我们先前开发的信息中 提供有关健康的量身定制研究和教育的平台（让我们变得健康！）和STEM 开发（STEM评估和报告跟踪器；开始）。拟议的项目利用数据 这些信息工具到有关人口统计数据收集的表面对话的可视化输出 以及与代表STEM教育，研究和的跨机构合作伙伴的负责任报告 多样化的社区。包容性的人口统计允许识别被排除在外的人， 边缘化或汇总不当，从而增加了解决STEM和不平等现象的能力 生物医学研究培训。该协作项目共同旨在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）建立一个保持一致的培训协作 通过数据科学输出的定性研究，以增强潜水员生物医学的真实表示 劳动力； 3）表征人口统计数据如何用于做出决策并为实践提供信息。这 项目对圆桌会议的跨职业合作伙伴保持一致，以告知人口统计数据实践供STEM使用 程序。它为中学生提供多模式培训 数据科学，定性研究和应用道德规范，以增强 身份。最后，它建立了K-12学生和计划包含数据科学的能力，这成为 随着学生的发展，可转让的研究技能继续职业。我们的项目使STEM程序能够 估计流行和理解计划对人群不足的影响的影响，最终使 对于程序的STEM评估更容易，因为我们集体努力改善STEM参与度和持久性 对于潜水员的学生团体。由于受训人员没有进入具有平等访问，住宿或的培训计划 准备工作，包容性的人群措施可以为一组细微的计划成果提供信息，从而促进 人口认同与支持招募和保留之间的相交性研究 从历史上讲，人为生物医学研究的学生不足。