Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
Institute for Planetary Health Behaviour, University of Erfurt, Erfurt, Germany.
Nat Hum Behav. 2023 Sep;7(9):1462-1480. doi: 10.1038/s41562-023-01644-3. Epub 2023 Jul 17.
The proliferation of anti-vaccination arguments is a threat to the success of many immunization programmes. Effective rebuttal of contrarian arguments requires an approach that goes beyond addressing flaws in the arguments, by also considering the attitude roots-that is, the underlying psychological attributes driving a person's belief-of opposition to vaccines. Here, through a pre-registered systematic literature review of 152 scientific articles and thematic analysis of anti-vaccination arguments, we developed a hierarchical taxonomy that relates common arguments and themes to 11 attitude roots that explain why an individual might express opposition to vaccination. We further validated our taxonomy on coronavirus disease 2019 anti-vaccination misinformation, through a combination of human coding and machine learning using natural language processing algorithms. Overall, the taxonomy serves as a theoretical framework to link expressed opposition of vaccines to their underlying psychological processes. This enables future work to develop targeted rebuttals and other interventions that address the underlying motives of anti-vaccination arguments.
反疫苗论点的泛滥是许多疫苗接种计划成功的威胁。有效反驳相反的论点需要一种方法，不仅要解决论点中的缺陷，还要考虑态度根源——即驱动一个人对疫苗持反对态度的潜在心理属性。在这里，通过对 152 篇科学文章的预先注册系统文献综述和对反疫苗论点的主题分析，我们开发了一个层次分类法，将常见的论点和主题与 11 个态度根源联系起来，这些态度根源解释了为什么一个人可能会对疫苗接种表示反对。我们进一步通过结合人类编码和使用自然语言处理算法的机器学习，在 2019 年冠状病毒病反疫苗错误信息上验证了我们的分类法。总的来说，该分类法可作为一个理论框架，将对疫苗的表达反对与潜在的心理过程联系起来。这使得未来的工作能够开发有针对性的反驳和其他干预措施，以解决反疫苗论点的潜在动机。
