School of Communication & Journalism, Auburn University, Auburn, Alabama, USA.
School of Communication, Kookmin University, Seoul, South Korea.
Disaster Med Public Health Prep. 2022 Dec 21;17:e314. doi: 10.1017/dmp.2022.282.
Vaccine hesitancy impacts the ability to cope with coronavirus disease 2019 (COVID-19) effectively in the United States. It is important for health organizations to increase vaccine acceptance. Addressing this issue, this study aimed to predict citizens' acceptance of the COVID-19 vaccine through a synthetic approach of public segmentation including cross-situational and situational variables. Controlling for demographics, we examined institutional trust, negative attitudes toward, and low levels of knowledge about vaccines (ie, lacuna public characteristics), and fear of COVID-19 during the pandemic. Our study provides a useful framework for public segmentation and contributes to risk and health campaigns by identifying significant predictors of COVID-19 vaccine acceptance.
We conducted an online survey on October 10, 2020 ( = 499), and performed hierarchical regression analyses to predict citizens' COVID-19 vaccine acceptance.
This study demonstrated that trust in the Centers for Disease Control and Prevention (CDC) and federal government, vaccine attitude, problem recognition, constraint recognition, involvement recognition, and fear positively predicted COVID-19 vaccine acceptance.
This study outlines a useful synthetic public segmentation framework and extends the concept of lacuna public to the pandemic context, helping to predict vaccine acceptance. Importantly, the findings could be useful in designing health campaign messages.
疫苗犹豫在美国有效应对 2019 年冠状病毒病（COVID-19）的能力产生影响。对于卫生组织来说，提高疫苗接种率是非常重要的。为了解决这个问题，本研究旨在通过包括跨情境和情境变量的公共细分综合方法预测公民对 COVID-19 疫苗的接受程度。在控制人口统计学因素的情况下，我们研究了机构信任、对疫苗的负面态度和疫苗知识水平低（即公共特征差距），以及大流行期间对 COVID-19 的恐惧。我们的研究为公共细分提供了一个有用的框架，并通过识别 COVID-19 疫苗接种接受度的重要预测因素，为风险和健康运动做出了贡献。
我们于 2020 年 10 月 10 日进行了一项在线调查（n=499），并进行了层次回归分析以预测公民对 COVID-19 疫苗的接受程度。
本研究表明，对疾病控制与预防中心（CDC）和联邦政府的信任、疫苗态度、问题识别、约束识别、参与识别和恐惧均与 COVID-19 疫苗接种接受度呈正相关。
本研究概述了一个有用的综合公共细分框架，并将公共特征差距的概念扩展到大流行背景，有助于预测疫苗的接受度。重要的是，这些发现可能有助于设计健康运动信息。
