Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations
DOI:
https://doi.org/10.21106/ijma.410Keywords:
- COVID-19,
- Coronavirus,
- Pandemics,
- Maternal and child health,
- MCH,
- Big data,
- Artificial intelligence
Abstract
As the global impact of the COVID-19 pandemic continues to evolve, robust data describing its effect on maternal and child health (MCH) remains limited. The aim of this study was to elucidate an agenda for COVID-19 research with particular focus on its impact within MCH populations. This was achieved using the Nominal Group Technique through which researchers identified and ranked 12 research topics across various disciplines relating to MCH in the setting of COVID-19. Proposed research topics included vaccine development, genomics, and artificial intelligence among others. The proposed research priorities could serve as a template for a vigorous COVID-19 research agenda by the NIH and other national funding agencies in the US.
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Published
2020-09-22
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How to Cite
Ikedionwu, C. A. , Dongarwar, D., Yusuf, K. K., Maiyegun, S. O., Ibrahimi, S., & Salihu, H. M. (2020). Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations. International Journal of Maternal and Child Health and AIDS, 9(3), 395–397. https://doi.org/10.21106/ijma.410
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Section
SHORT RESEARCH COMMUNICATION | COVID MCH RESEARCH AGENDA
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Copyright © 2020 Chioma A Ikedionwu, Deepa Dongarwar, Korede K. Yusuf, Sitratullah O. Maiyegun, Sahra Ibrahimi, Hamisu M. Salihu. Published by Global Health and Education Projects, Inc., USA.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , which permits others to remix, transform, and build upon the work non-commercially, provided the original author is credited and the new creations are licensed under the same terms.