Profiling lockdown adherence and poor coping responses towards the COVID-19 crisis in an international cross-sectional survey

  1. Sylvia Van Belle
  2. Amelia Dahlén
  3. Helgi B. Schiöth
  4. Samantha J. Brooks

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Abstract

This study uses international respondents to a COVID-lockdown related questionnaire (n = 1,688) to assess the determinants of adherence and poor coping in response to lockdown measures. A regression analysis was used to compare the relative importance of clusters derived from a K-means cluster analysis as well as various demographics (age, gender, level of education, political affiliation, a factor reflecting social security and a factor reflecting the lockdown harshness). Three distinct clusters (“General Population”, “Extreme Responders” and “Sufferers) were identified, corresponding well to a previous study. Clusters appeared to be the best overall predictors of coping and adherence although gender, political affiliation and lockdown harshness were also important predictors. The large proportion of variance that remains unexplained, combined with the relatively weak effects of traditional demographics, suggest that less concrete variables such as personality traits, health and environmental factors may be better predictors of adherence and coping during a pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2021.07.21.21260910: (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
    A discussion of the relative advantages and disadvantages, as well as their respondent demographics, are available from Prolific’s website [23].
    Prolific’s
    suggested: None
    Open-ended questions, particularly “highest level of education”, required extensive codification for standardization which was semi-automated in MySQL using Oracle MyQSL Workbench (v8.0 CE) and custom Python 3 scripts (algorithms available on request).
    Python
    suggested: (IPython, RRID:SCR_001658)
    All analyses were run in IBM SPSS (v27.0) and graphs were created in GraphPad Prism (v6.01.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Figures were created in Adobe Illustrator.
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Other limitations of this study include low variability in responses suggests that the reliability and ecological validity of the self-reported data should be questioned. Furthermore, the heavy skew towards young individuals, and lack of representation of nonbinary genders (who may be at increased risk for adversity during pandemic restrictions), limit the generalisability of our findings across these dimensions. Data regarding self-identified race, culture and ethnicity was not collected, and collecting this information as well as clearer or more standardised measures of socio-economic status and level of education would enhance the quality of the results found here. To improve the validity of future cluster analyses for COVID-related decision making, the model would benefit by keeping variables used for cluster creation and variables used for outcome measures mutually exclusive. In closing, each country has approached the COVID-19 pandemic in different ways, yet we demonstrate here that people have formed internationally coherent clusters in responding to this challenging period. Incorporating group-specific approaches to improve adherence and coping will not only assist in reducing the spread of infection, but also benefit the well-being of citizens while vaccinations are under way. This knowledge should inform way we create, enforce and adapt restrictive regulations for future large-scale crises.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.07.21.21260910v1 on medRxiv