Evidence map›Paper›PMID 36175993›Full record

ArticleBMC public health2022

The psychometric properties and the factorial structure of COVID-19 Vaccines Acceptance scale (VAC-COVID-19) within the Arabic language in a Palestinian contex.

Fayez Mahamid, Guido Veronese

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 40% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 2 institutions in 2 countries.

Fayez MahamidPsychology and Counseling Department, An-Najah National University, Nablus, Palestine. Mahamid@najah.edu.
Guido VeroneseUniversity of Milano, Bicocca, Italy.
An-Najah National University · PSUniversity of Milano-Bicocca · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 Vaccines Acceptance scale (VAC-COVID-19) is an international measure designed to evaluate vaccination acceptance against the COVID-19 virus. The current scale was translated from English to Arabic and validated within the Palestinian context.

aimsOur study aimed to test the factorial structure and the psychotic properties of the VAC-COVID-19 within the Palestinian context using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) through 484 participants selected using online method techniques.

findingsThe VAC-COVID-19 was a reliable and valid method in assessing COVID-19 vaccine acceptance among Palestinians. Results of CFA indicated a stable construct of a two-factor solution in assessing COVID-19 vaccination acceptance in a Palestinian context. (1) Reasons for not receiving the vaccination, and (2) for receiving the vaccination.

conclusionThe VAC-COVID-19 was a valid method to assess vaccination acceptance in the Arabic language within the Palestinian context. Therefore, it is recommended to conduct similar studies with diverse samples in Palestinian society; it would be prudent to target at-risk populations needed to develop the scale and its factorial structure. The VAC-COVID-19 can be a useful measure to assess vaccination acceptance among Palestinians, enabling health providers to implement interventions to modify negative attitudes toward not receiving vaccinations.

Indexed as

COVID-19COVID-19 VaccinesArabsHumansLanguagePsychometricsSurveys and QuestionnairesVaccinationCOVID-19 VaccinesCOVID-19PalestineTest validationVaccination acceptance

Identifiers

PMID36175993
PMCPMC9521561
OpenAlexW4298005224

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.