利用人工智能预测健康信念模型和COVID-19疫苗接种量,使用美国护士的调查文本
Samaneh Omranian1,2, Alireza Khoddam1, Celeste Campos-Castillo3
1Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Behavioral sciences (Basel, Switzerland)
|March 28, 2024
概括
人工智能确定了护士对COVID-19疫苗犹的关键因素. 像副作用这样的障碍对改变疫苗决策的影响最大.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 行为科学 行为科学
背景情况:
- 医疗保健提供者对疫苗的犹影响了公共卫生.
- 了解影响疫苗决策的因素至关重要.
研究的目的:
- 通过使用人工智能,调查女性护士中影响COVID-19疫苗犹的因素.
- 分析开放文本评论以确定疫苗犹的行为决定因素.
主要方法:
- 在38,788名女护士的纵向调查 (从2020年春季开始).
- 分析了1970年疫苗犹参与者的开放文本评论 (2021年1月和3月至4月).
- 应用了两种机器学习模型:一种是根据健康信念模型构造对评论进行分类,另一种是预测疫苗状态.
主要成果:
- 35%的怀疑接种疫苗的护士提到障碍,17%的严重程度和7%的易感性.
- 障碍物,如过敏反应和副作用,与从犹转向接种疫苗的转变最为相关.
- 一个机器学习模型根据最初的评论准确地预测了89%的疫苗状态.
结论:
- 人工智能有效地识别了导致COVID-19疫苗犹的行为因素.
- 解决感知障碍,特别是副作用,可能是增加犹不决的医疗保健提供者接种疫苗的关键.
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