Omranian Samaneh, Khoddam Alireza, Campos-Castillo Celeste, Fouladvand Sajjad, McRoy Susan, Rich-Edwards Janet
Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
Behav Sci (Basel). 2024 Mar 7;14(3):217. doi: 10.3390/bs14030217.
We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in Spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March-April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1970 comments from VH participants and trained two machine learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three health belief model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March-April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.
我们通过研究医疗保健人员的开放性文本评论，调查了人工智能（AI）如何揭示影响其对新冠疫苗犹豫态度的因素。我们从2020年春季开始对三个全国队列中的38788名现任和前任女护士进行了一项纵向调查，以评估疫情如何影响她们的生计。在2021年1月以及3月至4月的调查中，邀请参与者提供开放性文本评论，并回答有关新冠疫苗接种的具体问题。调查中的一个封闭式问题确定了那些要么无意接种要么不确定是否会接种新冠疫苗的疫苗犹豫（VH）参与者。我们收集了VH参与者的1970条评论，并训练了两种机器学习（ML）算法来识别与VH相关的行为因素。第一个预测模型将每条评论归类为与采取疾病预防活动相关的三种健康信念模型（HBM）结构之一（障碍、严重性和易感性）。第二个预测模型使用1月份评论中的词汇来预测2021年3月至4月VH参与者的疫苗接种状态；疫苗接种状态的预测准确率为89%。我们的结果表明，35%的VH参与者提到了障碍，17%提到了严重性，7%提到了对接种新冠疫苗的易感性。在HBM结构中，提到诸如过敏反应和副作用等障碍的VH参与者，其疫苗接种状态从犹豫到后来接种疫苗的相关变化最大。
