Strategies to exiting the COVID-19 lockdown for workplace and school: A scoping review

  1. Daniela D'angelo
  2. Alessandra Sinopoli
  3. Antonello Napoletano
  4. Silvia Gianola
  5. Greta Castellini
  6. Andrea del Monaco
  7. Alice Josephine Fauci
  8. Roberto Latina
  9. Laura Iacorossi
  10. Katia Salomone
  11. Daniela Coclite
  12. Primiano Iannone

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  1. ScreenIT

    SciScore for 10.1101/2020.09.04.20187971: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationStage 3: Study selection: This scoping review will adopt the following inclusion criteria: (1) studies must refer to workers or students of all ages facing an epidemic/pandemic crisis; (2) articles that measure or discuss relaxation strategies to exit lockdown (mass quarantine) during an epidemic/pandemic crisis, (3) quantitative studies of any study design can be included (i.e. randomised controlled trials, cohort, case–control, quasi-experimental, cross-sectional, mathematical model), as well as editorials, letters, and commentaries.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A preliminary search, run on May 20th, 2020 was performed on MEDLINE to identify index terms and keywords.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    This search strategy was translated and tailored for use in biomedical databases (EMBASE, SciSearch) and in online grey literature databases (Google Scholar) through CASSTN system.
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Prevention), MedRxiv (https://www.medrxiv.org/), and BioRxiv (https://www.biorxiv.org/) preprint servers.
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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 10.1016/j.ssci.2020.105067
  3. Version published to 10.1101/2020.09.04.20187971v1 on medRxiv