Predictors of COVID-19 Vaccine Uptake in Healthcare Workers

  1. Petros Galanis
  2. Ioannis Moisoglou
  3. Irene Vraka
  4. Olga Siskou
  5. Olympia Konstantakopoulou
  6. Aglaia Katsiroumpa
  7. Daphne Kaitelidou

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Abstract

We assessed the uptake of a COVID-19 vaccine and associated factors in a sample of healthcare workers (HCWs).

Methods:

An on-line cross-sectional study with 885 HCWs was conducted in Greece during August 2021. We measured socio-demographic data of HCWs and attitudes towards vaccination and the COVID-19 pandemic. A convenience sample was used since the questionnaire was distributed through social media and emails.

Results:

The majority of HCWs were vaccinated against the COVID-19 (91.5%). Females and HCWs with a history of seasonal influenza vaccination had a greater probability to get a COVID-19 vaccine. Also, increased self-perceived knowledge regarding the COVID-19 pandemic and increased trust in COVID-19 vaccines were associated with COVID-19 vaccine uptake.

Conclusions:

Policymakers and scientists should develop novel strategies to improve COVID-19 vaccine uptake among HCWs.

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  1. Version published to 10.1097/jom.0000000000002463
  2. ScreenIT

    SciScore for 10.1101/2021.09.14.21263300: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: The on-line questionnaire was accompanied by a detailed explanation of the study aim and design, and HCWs provided informed consent to participate anonymously in the study.
    IRB: The Ethics Committee of Department of Nursing, National and Kapodistrian University of Athens approved the study protocol (reference number; 370, 02-09-2021).
    Sex as a biological variablenot detected.
    RandomizationWe decided to increase substantially the sample size to minimize random error.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    IBM SPSS Statistics for Windows, Version 21.0.
    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:
    Our study suffers from several limitations. Although our study population was large, we used a convenience sample which is not representative of HCWs in Greece. Additionally, response rate cannot be calculated since we conducted an on-line study. Moreover, vaccine uptake and other information were self-reported and social desirability to bias responses may exist. For instance, some HCWs may have falsely stated that they had received a COVID-19 vaccine. We used an anonymous on-line questionnaire to reduce this bias. Further, we investigated a variety of determinants of COVID-19 vaccine uptake and some of them had not been studied before. However, it is possible that there are other factors affecting COVID-19 vaccination. Future research may consider including other factors which may influence COVID-19 vaccine uptake, e.g. personality traits, social media variables, fake news, conspiracy theories, etc. Finally, as is always the case in cross-sectional studies, no causal relationships between independent variables and COVID-19 vaccine uptake can be established.

    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.

  3. Version published to 10.1101/2021.09.14.21263300v1 on medRxiv