RAPID: Evolution of Public Risk Perception and Mental Models Regarding COVID-19

在出现COVID-19等危机时，公众是关键的应对伙伴。新的威胁与公众有关，但往往理解不足，误解导致不适当的反应。澄清误解发生的时间和原因很重要，因为由此产生的行为可能导致疾病传播、供应短缺和不必要的医疗系统负担。核心是个人的心理模型，由个人对风险的相关信念或感知组成的直觉理论，可能与科学共识一致，也可能不一致。心智模型为人们如何构思风险、构建决策以及与风险相关的行为奠定了基础。该项目跟踪COVID-19大流行期间个人的风险感知、心理模型和风险行为，利用一个时间敏感的机会，在具体的公共卫生背景下推进公共风险应对危机的科学。主要目标是纵向跟踪COVID-19大流行期间个人的风险感知、心理模型和风险相关行为。次要目标是开发新的方法来处理和分析大样本心智模型数据，并让专家参与我们的方法和更大基础设施的需求。该项目利用现有数据和计划的调查数据收集，建立了一个纵向评估，能够捕捉风险感知，心理模型和行为的变化。这些调查使用自由列表，一种简单的人类学自由联想技术，来收集人们风险心理模型的大样本图片。研究团队使用自动化词汇分析工具来处理数据，并使用网络分析技术来绘制心理模型。这个奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

In crises such as the emergence of COVID-19, the public is a critical response partner. Novel threats are concerning to the public, but often poorly understood, with misunderstanding leading to inappropriate reactions. Clarifying when and why misperceptions occur is important because resulting behavior can contribute to disease spread, supply shortages, and unnecessary health-care system burden. Central are individual mental models, intuitive theories made up of related beliefs or perceptions individuals have about a risk, which may or may not align with scientific consensus. Mental models form a foundation for how people conceive risk, structure decisions, and their risk-related behaviors. This project follows individuals’ risk perceptions, mental models, and risk behaviors over the course of the COVID-19 pandemic, capitalizing on a time-sensitive opportunity to push forward the science on public risk responses to crises, within a concrete public health context. The primary goal is to longitudinally track risk perceptions, mental models, and risk-related behaviors within individuals over the course of the COVID-19 pandemic. Secondary goals are to develop new methodological approaches to process and analyze large-sample mental models data and engage experts on our approach and needs for larger infrastructure. The project leverages existing data and planned survey data collection, building out a longitudinal assessment to be able to capture changes in risk perceptions, mental models, and behaviors. The surveys use freelisting, a simple free-association technique from anthropology, to gather a large-sample picture of people’s risk mental models. The research team employs automated lexical analysis tools to process the data and network analytic techniques to map out the mental models. The team uses regression analysis to examine relationships among mental models, risk perceptions, behavior, and their change over time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.