Title of Presentation

Inclusive approaches for measuring demographics of underrepresented populations in STEM and biomedical research training programs

Presenter Biography

Stephanie Paris graduated magna cum laude from Portland State University with a BS in Science and a minor in biology. While pursuing her bachelor’s degree, she became a BUILD EXITO Scholar and joined a Research Learning Community directed by Dr. Lisa Marriott at the OHSU-PSU School of Public Health. During her undergraduate research experience she studied the impact of stable housing on health outcomes for low-income individuals; statistically analyzed pre-post data to be used for presentation and future publication; supported development of Institutional Review Board protocols governing protections for human subjects; performed user testing of a health informatics platform (Let’s Get Healthy!) for global use; and contributed to a manuscript in preparation for peer-reviewed publication on the topic of epigenetics education for adolescents.

One of the many things that Stephanie learned about herself as an undergraduate research trainee is that writing provides a meaningful overlap of her skills and what she likes to do. For this reason, in 2021 she became a part time graduate student at PSU, pursuing a Masters in Professional and Technical Writing. She enjoys the challenge of coherently articulating complex concepts, and the potential to use a variety of dissemination methods for health promotion, particularly in populations that face barriers to the social determinants of health. Stephanie currently works as a Research Assistant in the Marriott Lab where she continues to foster her growing enthusiasm for the fascinating world of biomedical research.

Institution

OHSU

Program/Major

Public Health

Degree

MSPTW at PSU

Presentation Type

Presentation

Start Date

6-4-2022 1:45 PM

End Date

6-4-2022 1:56 PM

Keywords

disability, disadvantaged background, ethnicity, gender identity, gender minorities, health inequities, race, sexual minorities, underrepresented populations, workforce

Abstract

Increasing diversity within the biomedical workforce has been prioritized by federal strategic plans. The National Institutes of Health (NIH) expanded definitions of underrepresented populations in biomedical science (NOT-OD-20-031), though training programs remain challenged in approaches for accurately measuring and evaluating diversity. This study examined ways to measure demographic variables used in scientific literature and by research stakeholders. Gender, race/ethnicity, disability, and disadvantaged background were prioritized for comparison given their focus by NIH, with opportunities for stakeholders to identify additional demographic variables important in their work. Gender, sex, and sexual minorities were largely absent from programs’ demographic practices and warrant greater inclusion, consistent with recommendations from a NIH-commissioned report calling for routine collection of gender, sex, and sexual orientation information using standardized language. Oregon Health Authority’s Race, Ethnicity, Language, and Disability (REALD) offers a validated tool for improving resolution of racial/ethnic data and allows measurement of functional limitation in disability. The REALD tool can be merged with NIH categories for reporting. NIH re-defined ‘disadvantaged background’ yet first-generation college student status and rural eligibility were significantly underreported by trainees when verified. Inclusive demographics permit identification of individuals who are being excluded, marginalized, or improperly aggregated, increasing capacity to address inequities in biomedical research training. 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 and supporting the recruitment and retention of underrepresented students in biomedical research.

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Apr 6th, 1:45 PM Apr 6th, 1:56 PM

Inclusive approaches for measuring demographics of underrepresented populations in STEM and biomedical research training programs

Increasing diversity within the biomedical workforce has been prioritized by federal strategic plans. The National Institutes of Health (NIH) expanded definitions of underrepresented populations in biomedical science (NOT-OD-20-031), though training programs remain challenged in approaches for accurately measuring and evaluating diversity. This study examined ways to measure demographic variables used in scientific literature and by research stakeholders. Gender, race/ethnicity, disability, and disadvantaged background were prioritized for comparison given their focus by NIH, with opportunities for stakeholders to identify additional demographic variables important in their work. Gender, sex, and sexual minorities were largely absent from programs’ demographic practices and warrant greater inclusion, consistent with recommendations from a NIH-commissioned report calling for routine collection of gender, sex, and sexual orientation information using standardized language. Oregon Health Authority’s Race, Ethnicity, Language, and Disability (REALD) offers a validated tool for improving resolution of racial/ethnic data and allows measurement of functional limitation in disability. The REALD tool can be merged with NIH categories for reporting. NIH re-defined ‘disadvantaged background’ yet first-generation college student status and rural eligibility were significantly underreported by trainees when verified. Inclusive demographics permit identification of individuals who are being excluded, marginalized, or improperly aggregated, increasing capacity to address inequities in biomedical research training. 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 and supporting the recruitment and retention of underrepresented students in biomedical research.