Pandemic Publishing: Medical journals drastically speed up their publication process for Covid-19 is a research paper (2020). On theSindex it has a DataRank of 0.538. It has been cited 35 times.
In times of public crises, including the current Covid-19 pandemic, rapid dissemination of relevant scientific knowledge is of paramount importance. The duration of scholarly journals’ publication process is one of the main factors hindering quick delivery of new information. While proper editorial assessment and peer review obviously require some time, turnaround times for medical journals can be up to several months, which is undesirable in the era of a crisis. Following initiatives of medical journals and scholarly publishers to accelerate their publication process, this study assesses whether medical journals have indeed managed to speed up their publication process for Covid-19 related articles. It studies the duration of 14 medical journals’ publication process both during and prior to the current pandemic. Assessing a total of 669 articles, the study concludes that medical journals have indeed drastically accelerated the publication process for Covid-19 related articles since the outbreak of the pandemic. Compared to articles published in the same journals before the pandemic, turnaround times have decreased on average by 49%. The largest decrease in number of days between submission and publication of articles was due to a decrease in the number of days required for peer review. For articles not related to Covid-19, no acceleration of the publication process is found. While the acceleration of journals’ publication process is laudable from the perspective of quick information dissemination, it also raises concerns relating to the quality of the peer review process and the quality of the resulting publications.
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Base Score Contribution
0.538
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.