Physician Perceptions of Catching COVID-19: Insights from a Global Survey

  1. P. Murali Doraiswamy
  2. Mohan M. Chilukuri
  3. Dan Ariely
  4. Alexandra R. Linares

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Abstract

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  1. Version published to 10.1007/s11606-021-06724-6
  2. ScreenIT

    SciScore for 10.1101/2021.01.15.20249089: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: Following online informed consent, this survey aimed to sample 1,000 doctors equally divided between USA, EU and rest of the world (RoW) on many topics of which one question pertained to perceived risk.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite limitations such as cross-sectional nature, single question, and inability to control for all possible confounders, this survey provides foundational insights into physicians’ risk perception for COVID-19. Prospective studies of both actual and perceived risk across different medical specialties may enhance occupational safety.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.01.15.20249089 on medRxiv