This project builds capacity in inclusive data science for K-12 students and programs that support STEM (Science, Technology, Education, and Mathematics). Middle and high school students engage in research and interprofessional training that aligns qualitative research, data science, and applied ethics for enhancing accurate data collection and authentic representation of the biomedical workforce. This project applies iterative data feedback loops with collaborative partners to characterize responsible practices for collection and use of inclusive demographic data, which has the power to identify key demographic groups facing barriers to participation and retention in STEM programs needed to broaden and diversify the biomedical workforce.
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.