How Do Religious and Political Beliefs Predict COVID-19 Vaccination Behavior Among U.S. College Students? A Study Using the Health Belief Model.

Abstract

Predicting COVID-19 vaccination behavior among U.S. college students using the Health Belief Model (HBM). Cross-sectional survey. Online. A convenience sample of students in a public university in the U.S. (N = 411). Demographics; COVID-19 vaccination behavior as outcome variable; HBM variables (perceived threat of COVID-19, perceived individual benefit of vaccination, perceived community benefit of vaccination, perceived vaccine-safety barrier, perceived vaccination-cost barrier [time and effort], self-efficacy), and fear of COVID-19 as proximal predictors; religious beliefs and political beliefs as distal predictors. Questions/items measuring all variables in the survey data collection were taken from relevant and peer-reviewed publications and were modified to reflect the context of COVID-19. Structural equation modeling (SEM). The model fit the data very well (χ2/df = 2.27/5 = .45, p = .810; RMSEA = .000). Perceived individual benefit (β = .489, p < .001), perceived vaccine-safety barrier (β = -.151, p = .001), perceived vaccination-cost barrier (β = -.152, p < .001), and political beliefs (β = -.094, p = .029) are significant predictors of vaccination behavior. Effects of religious beliefs are completely, and effects of political beliefs are partially mediated by perceived individual benefit and the two barrier variables. Perceived individual benefit, the two barrier variables, and political beliefs are direct predictors, while religious beliefs are an indirect predictor, of COVID-19 vaccination behavior, suggesting that the HBM can effectively inform strategies to promote vaccination. Political beliefs are a much stronger predictor than religious beliefs. Students who are more religious or conservative tend to perceive less individual benefit and greater barriers to vaccination, making them less likely to get vaccinated. A limitation of this study is the disproportionate number of female participants (77.9%).