Psychosocial Predictors of Intention to Vaccinate Against the Coronavirus (COVID-19)

Abstract

The COVID-19 pandemic has wreaked havoc across the world. Public health efforts to combat the disease and return life to normalcy largely rests upon COVID-19 vaccination distribution and uptake. Thus, it is critical to examine factors that predict people’s intentions to vaccinate. This study explored predictors of intention to vaccinate against COVID-19 among demographic and personal factors, health behaviors and beliefs, COVID-19-specific beliefs, and trust in physicians, using a sample of U.S. adults. We employed bivariate correlations and hierarchical regression to analyze the data. We found that the strongest predictors are political orientation, trust in physicians, subjective norms, and prior flu shot uptake. These associations suggest that individuals who held more liberal political views, expressed higher levels of trust in their primary care provider, perceived stronger social pressure to vaccinate against COVID-19, and received a flu shot during the previous flu season, had a stronger intention to vaccinate against COVID-19. Based on our results, we suggest that public health efforts to increase vaccination uptake for COVID-19 vaccines focus on addressing political orientation (conservatism), involve primary care providers, emphasize vaccination as the norm (and not the exception), and use information about previous flu vaccinations to target vaccination campaigns.

Keywords:

• COVID-19
• Coronavirus
• vaccination intentions
• psychosocial
• vaccination attitudes
• political orientation
• physician trust
• social norm
• flu
• influenza

Acknowledgements

Ho Huynh would like to dedicate this work to Will Dunlop. “May he and I always be more than two ships passing in the night.” Ho Huynh would like to acknowledge Tom and Kathy Ellerson, Amanda, Logan Mai, and especially Levi Vinh, for their support during the manuscript writing process.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Financial support for this study was provided the Texas A&M University-San Antonio - College of Arts & Sciences Summer Faculty Research Fellowship 2020; and by the Texas A&M University-San Antonio Research Council Grant 2019–2020. Ágnes Zsila was supported by the ÚNKP-21-4 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.