Building a Systematic Online Living Evidence Summary of COVID-19 Research - 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; et al. (Journal of EAHIL, 2021)
Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 research publications. 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.