TY - JOUR
T1 - Building a Systematic Online Living Evidence Summary of COVID-19 Research
AU - The CAMARADES COVID-SOLES Group
AU - Hair, Kaitlyn
AU - Sena, Emily S.
AU - Wilson, Emma
AU - Currie, Gillian
AU - MacLeod, Malcolm
AU - Bahor, Zsanett
AU - Sena, Chris
AU - Ayder, Can
AU - Liao, Jing
AU - Tanriver Ayder, Ezgi
AU - Ghanawi, Joly
AU - Tsang, Anthony
AU - Collins, Anne
AU - Carstairs, Alice
AU - Antar, Sarah
AU - Drax, Katie
AU - Neves, Kleber
AU - Ottavi, Thomas
AU - Chow, Yoke Yue
AU - Henry, David
AU - Selli, Cigdem
AU - Fofana, Mariam
AU - Rudnicki, Martina
AU - Gabriel, Brendan
AU - Pearl, Esther J.
AU - Kapoor, Simran
AU - Baginskaite, Julija
AU - Shevade, Santosh
AU - Chung, Alexandria
AU - Przybylska , Marianna Antonia
AU - Henshall, David E.
AU - Lôbo Hajdu, Karina
AU - McCann, Sarah
AU - Sutherland, Catherine
AU - Lubiana Alves, Tiago
AU - Blacow, Rachel
AU - Hood, Rebecca J.
AU - Soliman, Nadia
AU - Harris, Alison
AU - Swift , Stephanie L.
AU - Rackoll, Torsten
AU - Percie du Sert, Nathalie
AU - Waldron, Fergal
AU - Macleod, Magnus
AU - Moulson , Ruth
AU - Low, Juin W.
AU - Rannikmae, Kristiina
AU - Miller , Kirsten
AU - Bannach-Brown, Alexandra
AU - Kerr, Fiona
AU - Hébert, Harry L
AU - Gregory, Sarah
AU - Shaw, Isaac William
AU - Christides, Alexander
AU - Alawady, Mohammed
AU - Hillary, Robert
AU - Clarke, Alex
AU - Jayasuriya, Natasha
AU - Sives, Samantha
AU - Nazzal, Ahmed
AU - Jayasuriya, Nimesh
AU - Sewell, Michael
AU - Bertani, Rita
AU - Fielding, Helen
AU - Drury, Broc
PY - 2021/6/24
Y1 - 2021/6/24
N2 - 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.
AB - 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.
UR - http://dx.doi.org/10.32384/jeahil17465
U2 - 10.32384/jeahil17465
DO - 10.32384/jeahil17465
M3 - Article
SN - 1841-0715
VL - 17
JO - Journal of the European Association for Health Information and Libraries
JF - Journal of the European Association for Health Information and Libraries
IS - 2
ER -