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Predictors of COVID-19 Vaccine Acceptance among Healthcare Workers in Nigeria.
- Source :
- Vaccines; Oct2022, Vol. 10 Issue 10, pN.PAG-N.PAG, 13p
- Publication Year :
- 2022
-
Abstract
- Healthcare workers (HCWs) are regarded as role models regarding health-related issues, including vaccination. Therefore, it is essential to identify the predictors for COVID-19 vaccine acceptance among them. A cross-sectional study to assess the risk perception, attitudes and knowledge of HCWs toward COVID-19 vaccination was carried out. A total of 710 responses were received between September 2021 and March 2022, from HCWs in the Northern, Western and Eastern regions of Nigeria. Cross tabulations were performed to determine statistical relations between sociodemographic variables, knowledge, attitudes and risk perceptions concerning COVID-19 vaccine acceptance. Multinomial logistic regression analysis was performed to determine the predictive variables for COVID-19 vaccine acceptance. Statistical analyses were performed and P-values less than 0.05 were considered statistically significant at a CI of 95%. Results showed that 59.3% of the participants were amenable to COVID-19 vaccines. Multinomial regression analysis identified 14 variables at α < 0.05 as predictors for vaccine acceptance. Male HCWs were 2.8 times more likely to accept the vaccine than their female counterparts. HCWs that were knowledgeable of the different kinds of vaccines, were willing to recommend the vaccines to their patients, believed that the timing of COVID-19 vaccination was appropriate and had recent vaccination history within three years were 1.6, 24.9, 4.4 and 3.1 times more likely to take COVID-19 vaccine than those not sure. The study found a relatively high trust (51.3%) in the Nigerian Center for Disease Control (NCDC) for information regarding COVID-19 vaccines. Therefore, the NDCD should disseminate more robust insights regarding the safety profiles of various COVID-19 vaccines. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2076393X
- Volume :
- 10
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- Vaccines
- Publication Type :
- Academic Journal
- Accession number :
- 159962253
- Full Text :
- https://doi.org/10.3390/vaccines10101645