Inclusive Approaches for Measuring Demographics of Underrepresented Populations in STEM and Biomedical Research Training Programs

As federal strategic plans prioritize increasing diversity within the biomedical workforce, and STEM training and outreach programs seek to recruit and retain students from historically underrepresented populations, there is a need for interrogation of traditional demographic descriptors and careful consideration of best practices for obtaining demographic data. To accelerate this work, equity-focused researchers and leaders from STEM programs convened to examine approaches for measuring demographic variables. Gender, race/ethnicity, disability, and disadvantaged background were prioritized given their focus by federal funding agencies. Categories of sex minority, sexual (orientation) minority, and gender minority (SSGM) should be included in demographic measures collected by STEM programs, consistent with recommendations from White House Executive Orders and federal reports. Our manuscript offers operationalized phrasing for demographic questions and recommendations for use across student-serving programs. Inclusive demographics permit the 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 welcome trainees and inform a nuanced set of program outcomes that facilitate research on intersectionality to support the recruitment and retention of underrepresented students in biomedical research.


Welcome! We are so glad you're here.
• This presentation is a starting place for ongoing conversations about how to measure demographics. • Conversations can be uncomfortable. Comfort and growth rarely co-exist. • Ask questions and for advice.
It is a great thing to want to learn and improve.
In the next ten minutes, we hope you will… • Recognize that research definitions of diversity evolve, reflecting the process of science. • Recognize flexibility in demographic data collection is needed. • Identify and share existing tools and resources for measuring demographics. • Stretch goal: Develop better strategies and tools to compassionately gather data for populations that are being excluded, marginalized, or improperly aggregated in biomedical research training programs.

Normalizing Feelings
Learning Objectives

Content Anytime
Follow along with the slides on our presentation website. https://sites.google.com/view/inclusivedemographics

Introvert's Delight!
Use Jamboard to anonymously ask a question or share comments, thoughts, resources or concerns. NIH Strategic Plan (2021-2025 Trainees do not enter training programs with equal access, accommodations, or preparation.

Introduction
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 science. Hofstra B, Kulkarni VV, Galvez SM, He B, Jurafsky D, McFarland DA. (2020). The Diversity-Innovation Paradox in Science. Proceedings of the National Academy of Sciences. 2020 Apr 28;117 (17) This exclusion may be intentional or unintentional, but may impact data quality, how we perceive, understand, or report on public and population health, the equity of programs, funding, public policy, and much more.
Image by Gerd Altmann from Pixabay

As defined by the National Institutes of Health (NOT-OD-20-031)
• Racial and ethnic groups underrepresented in biomedical research are defined as those that are ○ Black or African American ○ Hispanic or Latino ○ American Indian or Alaska Native ○ Native Hawaiian or other Pacific Islander • Individuals with disabilities are defined as those with a physical or mental impairment that substantially limits one or more major life activities • Individuals from disadvantaged backgrounds are defined as those who meet two or more of the following criteria: ○ Experienced past or present homelessness ○ Previously or presently in the

Race & Ethnicity
Why does it matter?
Not everyone receives the same access to or quality of healthcare, which results in race, ethnicity, language, and disability minority groups experiencing avoidable health inequities.
Greater resolution of data collection practices can help: • Identify health inequities in subpopulations • Guide development of culturally specific and accessible services • Guide equitable allocation of resources to address inequities In an effort to improve demographic data collection standards, Oregon Health Authority's Race, Ethnicity, Language, and Disability (REALD) offers a validated tool for collecting demographic information on race, ethnicity, language, and disability.

Disability (Health and Service Differences)
Disability data collection helps to identify health and service differences to eliminate preventable social and health inequities.

Sex & Gender
Why does it matter?
Conflating sex and gender in research practices excludes entire populations whose physical characteristics and/or identities do not fit within the constraints of male/female.
The erasure of sex and gender minority groups makes it impossible to provide services and accurate data for these populations.
Measuring sex and gender as independent variables allows researchers to collect more accurate data.
Open-ended prompts are most supportive of gender and sex diversities and can be qualitatively coded.
If coding an open-ended prompt is not possible (i.e., large studies), we suggest using the two question approach from Morrison, Dinno, & Salmon (2021).
What is your gender identity? (select all that apply): Are you transgender?

Conclusion
Big Picture 1) Research definitions of diversity evolve, reflecting the process of science. There are still populations that are being excluded, marginalized, or improperly aggregated. Look for them. Include them. Advance science.
2) Flexibility in demographic data collection is needed (e.g., due to time burden, participate age, funder requirements, program reach, etc.). Consider ways to be more inclusive in your data collection approach.
3) Be kind to yourself and others as we all learn to do better. This is the process of science.
Image by Gordon Johnson from Pixabay