A national cross-sectional survey of public perceptions of the COVID-19 pandemic: Self-reported beliefs, knowledge, and behaviors

  1. Jeanna Parsons Leigh
  2. Kirsten Fiest
  3. Rebecca Brundin-Mather
  4. Kara Plotnikoff
  5. Andrea Soo
  6. Emma E. Sypes
  7. Liam Whalen-Browne
  8. Sofia B. Ahmed
  9. Karen E. A. Burns
  10. Alison Fox-Robichaud
  11. Shelly Kupsch
  12. Shelly Longmore
  13. Srinivas Murthy
  14. Daniel J. Niven
  15. Bram Rochwerg
  16. Henry T. Stelfox

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Abstract

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  1. Version published to 10.1371/journal.pone.0241259
  2. ScreenIT

    SciScore for 10.1101/2020.07.07.20147413: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Statistical significance was set at α=0.05. Ethical Oversight: Dalhousie University (#2020-5121) and the University of Calgary (#20-0538) Research Ethics Boards approved this study.
    Consent: Prior to entering the questionnaire, respondents reviewed an informed consent page; consent was implied by completing the questionnaire.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We conducted all quantitative data analyses using SPSS, version 23 and R, version 3.5.1.(29) We used the R package “survey”(30) was used to obtain weighted descriptive statistics and chi-squared tests, version 3.36.
    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:
    Limitations: Our survey has limitations. Although providing a broad snapshot of population, cross-sectional surveys capture relevant data at a single moment in time. In a rapidly changing landscape, it is expected that self-reported perceptions and behaviors would change with new information. The use of serial surveys(13, 14) is one strategy to strengthen cross-sectional survey designs. At the same time, our study provides useful descriptive data at the height of the pandemic in Canada. Subsequent qualitative methodologies will further enrich our understanding of public actions and reactions to the COVID-19 pandemic. Second, as we elected to set a survey response quota, we are not able to determine a response rate. While there is a risk of non-response bias, the rapid collection of responses to reach our 2,000 quota (five days) and methodological strengths in our design (rigorous development including pre-testing and device agnosticism, large sample size, population representation and weighting by age, sex, and region) outweigh this limitation. Third, differences in public perceptions, knowledge, and behaviors that may be associated with socio-demographic factors such as age and gender were not addressed in this manuscript but will be the focus of future investigation. Finally, though overall results may be affected by larger numbers of respondents from Canada’s two largest provinces (Ontario and Québec), the weighting ensures results accurately reflect the actual regional po...

    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 10.1101/2020.07.07.20147413v1 on medRxiv