Factors of predicting the acceptance of the COVID-19 vaccine in West Africa
(a cross-sectional study in Nigeria)
1 Adeleke O.R.
1 Adegboro J.S.
1 Olofintuyi O.O.
1 Ayenigbara I.O.
1 Aina S.I.
1 Fadero E.O.
1 Oluwadare R.S.
1 Olaseyo T.
1 Department of Human Kinetics and Health Education, Adekunle Ajasin University, Akungba-Akoko
Background: The aim of this study was to examine the factors predicting the acceptance of COVID-19 vaccines in West Africa, with a focus on Nigeria. Four (4) hypotheses
were posed for this study.
Methods: This study employed a descriptive method design. The sample size was comprised of 32,224 respondents, all Nigeria citizens. A multistage sampling technique was employed for the survey involving quantitative data. A questionnaire was used as the instrument for data collection. The data collected for this study were analysed using both descriptive and inferential statistics (t-test and ANOVA).
Results: The findings of this study revealed that there is a significant difference in COVID-19 vaccine acceptance based on age F (9, 32214) = 812.114, P<0.05, ƞ2=0.1849; Factors of predicting the acceptance of the COVID-19 vaccine in West Africa (a cross-sectional study in Nigeria) based on gender differences t = (32222) = -21.808, P<0.05; based on religion t = (32222) = –75.228, P<0.05; and based on income F (4, 32219) = 740.394, P<0.05, ƞ2=0.084.
Conclusion: The findings of this study show that there is a significant relationship between age, gender, religion, income and the acceptance of COVID-19 vaccines in Nigeria. The findings further show that men are more vaccinehesitant than women; older people find it easier to accept vaccination; Christians are more likely to accept COVID-19 vaccines than people from other religions. It is therefore recommended that religious leaders should be well educated on the health benefit of the COVID-19 vaccination and that the government should put more effort into improving the economy of the country so that the individual income can improve.
Keywords: acceptance, COVID-19, pandemic, predicting factors,