Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations
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 achieve...
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| Main Authors: | , , , , , |
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| Format: | Book |
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Global Health and Education Projects, Inc.,
2020-09-01T00:00:00Z.
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| Summary: | 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. Key words: • COVID-19 • Coronavirus • Pandemics • Maternal and child health • MCH • Big data • Artificial intelligence Copyright © 2020 Ikedionwu et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited. |
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| Item Description: | 10.21106/ijma.410 2161-8674 2161-864X |