Self-learning on COVID-19 among medical students in Bhutan: A cross-sectional study

  1. Thinley Dorji
  2. Saran Tenzin Tamang
  3. T.V.S.V.G.K. Tilak

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Abstract

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  1. Version published to 10.1016/j.heliyon.2021.e07533
  2. ScreenIT

    SciScore for 10.1101/2020.09.07.20189936: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data processing and analysis: The data were extracted from Google Forms and analysed in STATA Version 13.1 (StataCorp, Stata Statistical Software).
    STATA
    suggested: (Stata, RRID:SCR_012763)
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    Study limitations: As the scenario of COVID-19 situation progressed in South Asia and in Bhutan, the perception of these medical students might have changed over time. This paper was an attempt to understand their knowledge and attitude towards their participation in COVID-19 response in the context of Bhutan.

    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 10.1101/2020.09.07.20189936v1 on medRxiv