Knowledge and beliefs towards universal safety precautions during the coronavirus disease (COVID-19) pandemic among the Indian public: a web-based cross-sectional survey

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Abstract

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  1. SciScore for 10.1101/2020.03.31.20047126: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Ethical Considerations: Participants were explained about the purpose of the study and requested to provide the consent of voluntary willingness before participating in the survey.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Analysis: Data were entered into Microsoft Excel spreadsheets and cross-checked for accuracy and the statistical analysis was performed using IBM SPSS software for windows, version 24 (Armonk, NY, USA) [23].
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    There are certain limitations that should be considered when interpreting the findings of the current study. Firstly, as this is a cross-sectional survey, causal inferences cannot be made, and chances for the recall and information bias may exist. Secondly, as the questionnaire is self-administered and thus, by depending upon the self-reported data, it is difficult to predict and understand whether the respondents are filling the survey honestly, i.e., social desirability bias and the responses provided by the participants may not reflect the reality. Lastly, as this is an internet-based online survey, it might not capture the responses from the regions with the restricted access to the social media, and thus may introduce demographic selection bias and might have received the responses mostly from the younger and internet-active population leading to coverage bias.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.