Using mobile phone-based text message to recruit representative samples: Assessment of a cross-sectional survey about the COVID-19 vaccine hesitation

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Abstract

No abstract available

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  1. SciScore for 10.1101/2022.01.15.22269259: (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
    All statistical analyses were conducted using Microsoft Excel Version 16.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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 of our study were based mostly on subjective interpretations about the feasibility. As our analysis compares representativeness with the population, it is impossible to directly compare the representativeness of samples recruited with mobile phone-based with different recruitment methods. Another perceived limitation was the population’s distrust in receiving non-official and unexpected phone messages. Moreover, we can not account for the real number of delivery messages due to limitations as internet failure, missing mobile phone numbers, or interrupted plan services, especially in the low-income population. We believe there are many potential applications of mobile phone-based advertising for researchers, governments, and community-based organizations who wish to learn about the populations they serve. Besides, we believe that these institutions should previously officially advertise the target population to promote a wider adhesion and confidence to respond to the survey.

    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

    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.