The impact of state paid sick leave policies on weekday workplace mobility during the COVID-19 pandemic

  1. C.C. Pollack
  2. A. Deverakonda
  3. F. Hassan
  4. S. Haque
  5. A.N. Desai
  6. M.S. Majumder

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Abstract

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  1. Version published to 10.1016/j.puhe.2022.08.019
  2. ScreenIT

    SciScore for 10.1101/2022.01.16.22269377: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data were aggregated with Python (version 3.8) and analyzed in R (version 4.0.3) using the RStudio Integrated Development Environment (version 1.3.1093).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    23 While this study is the first to examine the impact of pre-existing state PSL on weekday workplace mobility during the COVID-19 pandemic, it is not without its limitations. First, publicly available covariate data were compiled across multiple sources and may have been measured at different points in time; thus, future work should attempt to standardize the time frame of analysis. Second, analysis was limited to the early stages of the COVID-19 pandemic, presenting opportunities for examination of the long-term impacts of pre-existing state PSL on workplace mobility and other metrics. Third, given the ecological nature of the study, future work is necessary to quantify the direct, person-level impact of pre-existing state PSL on adherence to workplace mobility measures. Fourth, Google COVID-19 Community Mobility Reports may not be representative of all populations (e.g., those without access to a cellular device), and the calculation of daily changes relative to a baseline in January and February 2020 (as opposed to a full year) may result in some seasonal biases. This may bias results away from the null, as individuals may be less likely to take off work during January and February compared to the following months. Finally, this study is limited to PSL, and evaluation of additional economic policies––such as medical leave for family members, flexible work hours, remote work policies, and flexibility in shift work––could offer more nuanced perspectives. PSL is fundamental ...

    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.

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

    About SciScore

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  3. Version published to 10.1101/2022.01.16.22269377v1 on medRxiv