Knowledge, Attitude and Practices towards COVID-19 Guidelines among Students in Bangladesh

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Abstract

This paper explores the level of knowledge, attitude and practices of COVID-19 guidelines among the students in Bangladesh. In achieving this objective, this paper uses primary data collected from 1822 students and three different Likert scales and a one-way ANOVA test are used to assess knowledge, attitudes, and practice (KAP) scores and mean differences with respect to different variables. The research reveals that the majority of students have a higher level of knowledge and a positive attitude towards the COVID-19 guidelines. In contrary, only 0.22 percent students show strong compliance towards COVID-19 guidelines while majority students (60.54 percent) have rather a poor adherence which is an alarming finding. Reopening the educational institutions in Bangladesh is, therefore, not advisable yet and reinforcing the preventive measures through campaigns and online discussion to persuade people to follow the preventive guidelines is highly recommended to contain this global disease.

What We Already Know

  • Majority papers revealed the KAP of general people or medical related people.

  • Majority people have moderate level of knowledge towards COVID-19.

  • No research is found on KAP of the students in Bangladesh.

What This Article Adds

  • The KAP of COVID-19 guidelines among the students in Bangladesh.

  • Majority students have the higher level of knowledge towards COVID-19 guideline among students.

  • Only 0.22 percent students show strong compliance towards COVID-19 guidelines which recommends not reopening educational institutions now.

Article activity feed

  1. SciScore for 10.1101/2021.03.07.433083: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All analyses were conducted using SPSS 24 software.
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • No funding statement was detected.
    • 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.