Open AccessBuilding a Systematic Online Living Evidence Summary of COVID-19 Research
Author(s) -
Kaitlyn Hair,
Emily S. Sena,
Emma Wilson,
Gillian L. Currie,
Malcolm Macleod,
Zsanett Bahor,
Chris Sena,
Can Ayder,
Jing Liao,
Ezgi Tanriver-Ayder,
Joly Ghanawi,
Anthony Tsang,
Anne T. Collins,
Fergal M Waldron,
Sarah Antar,
Katie Drax,
Kleber Neves,
Thomas M. Ottavi,
Yoke Yue Chow,
David Henry,
Çiğdem Selli,
Mariam O. Fofana,
Martina Rudnicki,
Brendan M. Gabriel,
Esther J. Pearl,
Simran S Kapoor,
Julija Baginskaite,
Santosh Shevade,
Alexandria Chung,
Marianna Przybylska,
David Henshall,
Karina L. Hajdu,
Sarah McCann,
Catherine Sutherland,
Tiago Lubiana,
Rachel Blacow,
Rebecca J. Hood,
Nadia Soliman,
A.J. Harris,
Stephanie L. Swift,
Torsten Rackoll,
Nathalie Percie du Sert,
Fergal M Waldron,
Magnus Macleod,
Ruth Moulson,
Juin W. Low,
Kristiina Rannikmäe,
K. Miller,
Alexandra BannachBrown,
Fiona Kerr,
Harry L. Hébert,
Sarah Gregory,
Isaac Shaw,
Alexander Christides,
Mohammed Alawady,
Robert F. Hillary,
Alex E. Clark,
Natasha Jayasuriya,
Samantha Sives,
Ahmed Nazzal,
Nimesh Jayasuriya,
Michael D E Sewell,
Rita Bertani,
Hannah Fielding,
Broc Drury
Publication year - 2021
Publication title -
journal of the european association for health information and libraries
Language(s) - English
Resource type - Journals
eISSN - 2392-8131
pISSN - 1841-0715
DOI - 10.32384/jeahil17465
Subject(s) - covid-19 , pace , relevance (law) , workflow , pandemic , data science , crowdsourcing , quality (philosophy) , computer science , world wide web , political science , medicine , geography , philosophy , disease , geodesy , epistemology , pathology , database , virology , outbreak , infectious disease (medical specialty) , law
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.