Evidence map›Paper›PMID 36992140›Full record

ArticleVaccines2023

Deepening Our Understanding of COVID-19 Vaccine Decision-Making amongst Healthcare Workers in Southwest Virginia, USA Using Exploratory and Confirmatory Factor Analysis.

Jesse Bendetson, Mandy C Swann, Alicia Lozano, Jennifer West, Alexandra L Hanlon, Ian Crandell, Maimuna Jatta, Charles J Schleupner, Anthony Baffoe-Bonnie

Abstract read
In one paragraph

Article in Vaccines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jesse BendetsonVirginia Tech Carilion School of Medicine, Roanoke, VA 24016, USA.ORCID 0000-0002-9651-0181
Mandy C SwannInfection Prevention and Control Section, Carilion Clinic, Roanoke, VA 24014, USA.ORCID 0000-0003-0348-8024
Alicia LozanoVirginia Tech Center for Biostatistics and Health Data Science, Roanoke, VA 24016, USA.
Jennifer WestVirginia Tech Center for Biostatistics and Health Data Science, Roanoke, VA 24016, USA.
Alexandra L HanlonVirginia Tech Center for Biostatistics and Health Data Science, Roanoke, VA 24016, USA.
Ian CrandellVirginia Tech Center for Biostatistics and Health Data Science, Roanoke, VA 24016, USA.
Maimuna JattaInfection Prevention and Control Section, Carilion Clinic, Roanoke, VA 24014, USA.ORCID 0000-0002-5160-1792
Charles J SchleupnerVirginia Tech Carilion School of Medicine, Roanoke, VA 24016, USA.
Anthony Baffoe-BonnieVirginia Tech Carilion School of Medicine, Roanoke, VA 24016, USA.ORCID 0000-0002-6316-7719

Funding

The integrated Translational Health Research Institute of Virginia (iTHRIV): Using Data to Improve HealthUL1TR003015 · NCATS · UNIVERSITY OF VIRGINIA · PI BROWN, DONALD E, JOHNSTON, KAREN C. · 2019 to 2023
$20.3M
Institutional Career Development CoreKL2TR003016 · NCATS · UNIVERSITY OF VIRGINIA · PI PAPIN, JASON, WORRALL, BRADFORD B · 2019 to 2023
$3.9M
NCATS NIH HHS KL2 TR003016NCATS NIH HHS KL2TR003016NCATS NIH HHS UL1 TR003015NCATS NIH HHS UL1TR003015
6 · The paper itself

Abstract

Vaccine hesitancy amongst healthcare workers (HCWs) has been a major challenge throughout the COVID-19 pandemic. While many studies have identified HCW characteristics and specific attitudes associated with COVID-19 vaccine hesitancy, researchers are still working towards developing a holistic understanding of the psychological constructs that influence COVID-19 vaccine decision-making in this population. Between 15 March and 29 March 2021, we distributed an online survey assessing individual characteristics and vaccine-related perceptions to employees of a not-for-profit healthcare system in Southwest Virginia (N = 2459). We then performed exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to describe patterns of vaccine-related thought amongst HCWs and identify latent psychometric constructs involved in vaccine decision-making. The goodness of model fit was assessed using the Tucker-Lewis Index (TLI), the Comparative Fit Index (CFI), and the Root Mean Square Error of Approximation (RMSEA). Internal consistency and reliability of each factor were assessed using Cronbach's alpha. EFA identified four latent psychometric constructs: Lack of trust in the COVID-19 vaccine; Anti-science sentiment; Adverse side-effects; and Situational risk assessment. The goodness of EFA model fit was adequate (TLI > 0.90, RMSEA ≤ 0.08) with acceptable internal consistency and reliability for three of four factors (Cronbach's alpha > 0.70). The CFA model also had adequate goodness of fit (CFI > 0.90, RMSEA ≤ 0.08). We believe the psychometric constructs identified in this study can provide a useful framework for interventions to improve vaccine uptake amongst this critical population.

Indexed as

COVID-19factor analysishealthcare workershesitancypsychometric constructsvaccine

Identifiers

PMID36992140
PMCPMC10058545

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.