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

Kaitlyn Hair | Zendy; Emily S. Sena | Zendy; Emma Wilson | Zendy; Gillian L. Currie | Zendy; Malcolm Macleod | Zendy; Zsanett Bahor | Zendy; Chris Sena | Zendy; Can Ayder | Zendy; Jing Liao | Zendy; Ezgi Tanriver-Ayder | Zendy; Joly Ghanawi | Zendy; Anthony Tsang | Zendy; Anne T. Collins | Zendy; Alice Carstairs | Zendy; Sarah Antar | Zendy; Katie Drax | Zendy; Kleber Neves | Zendy; Thomas Ottavi | Zendy; Yoke Yue Chow | Zendy; David Henry | Zendy; Çiğdem Selli | Zendy; Mariam O. Fofana | Zendy; Martina Rudnicki | Zendy; Brendan M. Gabriel | Zendy; Esther J. Pearl | Zendy; Simran S. Kapoor | Zendy; Julija Baginskaite | Zendy; Santosh Shevade | Zendy; Alexandria Chung | Zendy; Marianna Przybylska | Zendy; David Henshall | Zendy; Karina L. Hajdu | Zendy; Sarah K. McCann | Zendy; Catherine Sutherland | Zendy; Tiago Lubiana | Zendy; Rachel Blacow | Zendy; Rebecca J. Hood | Zendy; Nadia Soliman | Zendy; A.J. Harris | Zendy; Stephanie L. Swift | Zendy; Torsten Rackoll | Zendy; Nathalie Percie du Sert | Zendy; Fergal M. Waldron | Zendy; Magnus Macleod | Zendy; Ruth Moulson | Zendy; Juin Low | Zendy; Kristiina Rannikmäe | Zendy; K. Miller | Zendy; Alexandra BannachBrown | Zendy; Fiona Kerr | Zendy; Harry L. Hébert | Zendy; Sarah Gregory | Zendy; Isaac Shaw | Zendy; Alexander Christides | Zendy; Mohammed Alawady | Zendy; Robert F. Hillary | Zendy; Alex E. Clark | Zendy; Natasha Jayasuriya | Zendy; Samantha Sives | Zendy; Ahmed Nazzal | Zendy; Nimesh Jayasuriya | Zendy; Michael D.E. Sewell | Zendy; Rita Bertani | Zendy; Helen R. Fielding | Zendy; Broc Drury | Zendy

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Building 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,

Alice Carstairs,

Sarah Antar,

Katie Drax,

Kleber Neves,

Thomas 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 K. 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 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,

Helen R. Fielding,

Broc Drury

Publication year - 2021

Publication title -

journal of eahil

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.

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