Christopher S. Awad
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Christopher S. Awad has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Abiel Roche-Lima
Center for Collaborative Research in Health Disparities, RCMI Program, Medical Science Campus, University of Puerto Rico, San Juan, PR 00936-5067, USA
1 shared publication - John Kwagyan
Howard University College of Medicine
1 shared publication - Muhammed Yassin Idris
Morehouse School of Medicine Atlanta GA USA
1 shared publication - Paul Bernard Tchounwou
RCMI Center for Urban Health Disparities Research and Innovation, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD, United States
1 shared publication - Qingguo Wang
Meharry Medical College
1 shared publication - Youping Deng
Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA
1 shared publication
Latest Publications
- Open Access
- 24 December 2022
- Published by MDPI AG in International Journal of Environmental Research and Public Health
- Vol. 20 (1) , 279
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 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.
- 24 December 2022
- Published by MDPI AG in International Journal of Environmental Research and Public Health
- Vol. 20 (1) , 279
Open AccessSummary 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 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.