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Research Spending & Results

Award Detail

PD/PI:
  • Marie Plaisime
Award Date:07/29/2021
Estimated Total Award Amount: $ 138,000
Funds Obligated to Date: $ 138,000
  • FY 2021=$138,000
Start Date:08/15/2021
End Date:07/31/2023
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Moving Beyond Bias: Structural Competency in Medical Education
Federal Award ID Number:2105430
DUNS ID:NR
Program:SPRF-Broadening Participation
Program Officer:
  • Josie S. Welkom
  • (703) 292-7376
  • jwelkom@nsf.gov

Awardee Location

Street:
City:Washington
State:DC
ZIP:20059-0001
County:Washington
Country:US
Awardee Cong. District:

Primary Place of Performance

Organization Name:Harvard University
Street:
City:Boston
State:MA
ZIP:02115-6009
County:Boston
Country:US
Cong. District:07

Abstract at Time of Award

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the co-sponsorship of Dr. Mary Bassett of Harvard University and Professor Dorothy Roberts of the University of Pennsylvania, this postdoctoral fellowship award supports an early career scientist investigating how structural racism, race-based medicine, and racial bias influence micro- and macro- processes in clinical interactions. Health provider bias, stereotyping, and discrimination may undermine patient care and contribute to poor health outcomes in racially stigmatized groups. Previous researchers have posited several theoretical models and testable hypotheses; however, less is known about the multilevel sociological environments in which clinical decisions are made and how cognitive biases can translate to racial differential treatment and health disparities. Medical education has struggled with confronting how structural forces impact racialized groups within the discipline. This study uses sophisticated quantitative modeling and qualitative approaches to explore beyond individual behaviors and beliefs by investigating factors that shape clinical decisions and patient-provider relationships. The overall goal of the project, entitled Moving Beyond Bias: Structural Competency in Medical Education, is to advance scientific knowledge of structural competency, bias, and patient-provider interaction while enhancing theories and methodological approaches to improve medical curricula, policies, and practices. This study employs a mixed-method approach across two phases to assess medical providers' perceptions of structural competency pedagogy and their understanding of structural racism in medical education. Phase 1 uses qualitative approaches to examine medical students' and residents' training on racial bias and race-based medicine, especially among those identified as Black, Indigenous, and People of Color (BIPOC), women, and/or persons with disabilities. Phase 2 involves implementing a randomized control trial (RCT), informed by data collected in Phase 1, to assess structural factors that impact provider recommendations. We will explore how medical students and faculty can reduce bias and race-based learning across medical institutions using quantitative, computational, & mixed methodological (QCM) tools and use inferential statistics to test hypotheses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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