Knowledge and Perceptions of COVID-19 Among Health Care Workers: Cross-Sectional Study

  1. Akshaya Srikanth Bhagavathula
  2. Wafa Ali Aldhaleei
  3. Jamal Rahmani
  4. Mohammadjavad Ashrafi Mahabadi
  5. Deepak Kumar Bandari

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Abstract

During the first week of March, the coronavirus disease 2019 (COVID-19) outbreak reached more than 100 countries with over 100,000 cases. Health care authorities have already initiated awareness and preparedness activities worldwide. A poor understanding of the disease among health care workers (HCWs) may result in delayed treatment and result in the rapid spread of the infection.

Objective

This study aimed to investigate the knowledge and perceptions of HCWs about COVID-19.

Methods

A cross-sectional, web-based study was conducted among HCWs about COVID-19 during the first week of March 2020. A 23-item survey instrument was developed and distributed randomly to HCWs using social media; it required 5 minutes to complete. A chi-square test was used to investigate the level of association among variables, with significance set to P<.05.

Results

Of 529 participants, a total of 453 HCWs completed the survey (response rate: 85.6%); 51.6% (n=234) were male, 32.1% (n=147) were aged 25-34 years, and most were doctors (n=137, 30.2%) and medical students (n=134, 29.6%). Most participants (n=276, 61.0%) used social media to obtain information on COVID-19. A significant proportion of HCWs had poor knowledge of its transmission (n=276, 61.0%) and symptom onset (n=288, 63.6%) and showed positive perceptions of COVID-19. Factors such as age and profession were associated with inadequate knowledge and a poor perception of COVID-19.

Conclusions

As the global threat of COVID-19 continues to emerge, it is critical to improve the knowledge and perceptions of HCWs. Educational interventions are urgently needed to reach HCWs worldwide, and further studies are warranted.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.09.20033381: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Informed consent was obtained from each participant prior to participation.
    RandomizationThe developed draft survey instrument was distributed to ten randomly selected faculty members to assess its readability and validity before pretesting among 20 randomly selected HCWs for clarity, relevance, and acceptability.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: The obtained data were coded, validated, and analyzed using SPSS version 24 (IBM, Armonk, NY, USA).
    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.
    • 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.

  2. Version published to 10.2196/19160
  3. Version published to 10.2196/preprints.19160
  4. Version published to 10.1101/2020.03.09.20033381v2 on medRxiv
  5. Version published to 10.1101/2020.03.09.20033381v1 on medRxiv