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Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science

Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and train...

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Autores principales: Awad, Christopher S., Deng, Youping, Kwagyan, John, Roche-Lima, Abiel, Tchounwou, Paul B., Wang, Qingguo, Idris, Muhammed Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819075/
https://www.ncbi.nlm.nih.gov/pubmed/36612607
http://dx.doi.org/10.3390/ijerph20010279
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author Awad, Christopher S.
Deng, Youping
Kwagyan, John
Roche-Lima, Abiel
Tchounwou, Paul B.
Wang, Qingguo
Idris, Muhammed Y.
author_facet Awad, Christopher S.
Deng, Youping
Kwagyan, John
Roche-Lima, Abiel
Tchounwou, Paul B.
Wang, Qingguo
Idris, Muhammed Y.
author_sort Awad, Christopher S.
collection PubMed
description Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce.
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spelling pubmed-98190752023-01-07 Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science Awad, Christopher S. Deng, Youping Kwagyan, John Roche-Lima, Abiel Tchounwou, Paul B. Wang, Qingguo Idris, Muhammed Y. Int J Environ Res Public Health Article Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce. MDPI 2022-12-24 /pmc/articles/PMC9819075/ /pubmed/36612607 http://dx.doi.org/10.3390/ijerph20010279 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Awad, Christopher S.
Deng, Youping
Kwagyan, John
Roche-Lima, Abiel
Tchounwou, Paul B.
Wang, Qingguo
Idris, Muhammed Y.
Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science
title Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science
title_full Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science
title_fullStr Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science
title_full_unstemmed Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science
title_short Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science
title_sort summary of year-one effort of the rcmi consortium to enhance research capacity and diversity with data science
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819075/
https://www.ncbi.nlm.nih.gov/pubmed/36612607
http://dx.doi.org/10.3390/ijerph20010279
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