PLoS ONE (Jan 2024)

Virtual BUILD Research Collaboratory: A biomedical data science training using innovative pedagogy to address structures of racism and inequitable stress for undergraduates of color.

  • Niquo Ceberio,
  • Peter Le,
  • Jasmón Bailey,
  • Sonthonax Vernard,
  • Nichole Coleman,
  • Yazmin P Carrasco,
  • Telisa King,
  • Kirsten Bibbins-Domingo,
  • Tung Nguyen,
  • Audrey Parangan-Smith,
  • Kelechi Uwaezuoke,
  • Robert C Rivers,
  • Kenjus Watson,
  • Leticia Márquez-Magaña,
  • Kala M Mehta

DOI
https://doi.org/10.1371/journal.pone.0294307
Journal volume & issue
Vol. 19, no. 2
p. e0294307

Abstract

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ObjectiveThe unprecedented events of 2020 required a pivot in scientific training to better prepare the biomedical research workforce to address global pandemics, structural racism, and social inequities that devastate human health individually and erode it collectively. Furthermore, this pivot had to be accomplished in the virtual environment given the nation-wide lockdown.MethodsThese needs and context led to leveraging of the San Francisco Building Infrastructure Leading to Diversity (SF BUILD) theories of change to innovate a Virtual BUILD Research Collaboratory (VBRC). The purpose of VBRC was to train Black, Indigenous, and people of color (BIPOC) students to apply their unique perspectives to biomedical research. These training activities were evaluated using a pre-post survey design that included both validated and new psychosocial scales. A new scale was piloted to measure culturally relevant pedagogy.ResultsVBRC scholars increased science identity on two items: thinking of myself as a scientist (+1point, p = 0.006) and belonging to a community of scientists (+1point, p = 0.069). Overall, scholars perceived stress also decreased over VBRC (-2.35 points, p = 0.02). Post VBRC, scholars had high agency scores (μ = 11.02, Md = 12, range = 6-12, σ = 1.62) and cultural humility scores (μ = 22.11, Md = 23, range = 12-24, σ = 2.71). No notable race/ethnic differences were found in any measures.ConclusionsTaken together, our innovative approach to data science training for BIPOC in unprecedented times shows promise for better preparing the workforce critically needed to address the fundamental gaps in knowledge at the intersection of public health, structural racism, and biomedical sciences.