Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for...

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Bibliographic Details
Published in:	Journal of the European Association for Health Information and Libraries Vol. 17; no. 2; pp. 21 - 26
Main Authors:	Hair, Kaitlyn, Sena, Emily S., Wilson, Emma, Currie, Gillian, Macleod, Malcolm, Bahor, Zsanett, Sena, Chris, Ayder, Can, Liao, Jing, Tanriver Ayder, Ezgi, Ghanawi, Joly, Tsang, Anthony, Collins, Anne, Carstairs, Alice, Antar, Sarah, Drax, Katie, Neves, Kleber, Ottavi, Thomas, Chow, Yoke Yue, Henry, David, Selli, Cigdem, Fofana, Mariam, Rudnicki, Martina, Gabriel, Brendan, Pearl, Esther J, Kapoor, Simran S, Baginskaite, Julija, Shevade, Santosh, Chung, Alexandria, Przybylska, Marianna Antonia, Henshall, David E, Hajdu, Karina Lôbo, McCann, Sarah, Sutherland, Catherine, Lubiana Alves, Tiago, Blacow, Rachel, Hood, Rebecca J., Soliman, Nadia, Harris, Alison, Swift, Stephanie L., Rackoll, Torsten, Percie du Sert, Nathalie, Waldron, Fergal, Macleod, Magnus, Moulson, Ruth, Low, Juin W., Rannikmae, Kristiina, Miller, Kirsten, Bannach-Brown, Alexandra, Kerr, Fiona, Hébert, Harry L, Gregory, Sarah, Shaw, Isaac William, Christides, Alexander, Alawady, Mohammed, Hillary, Robert, Clark, Alex, Jayasuriya, Natasha, Sives, Samantha, Nazzal, Ahmed, Jayasuriya, Nimesh, Sewell, Michael, Bertani, Rita, Fielding, Helen, Drury, Broc
Format:	Journal Article
Language:	English
Published:	24-06-2021
Online Access:	Get full text
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Summary:	Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence.
ISSN:	1841-0715 1841-0715
DOI:	10.32384/jeahil17465