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

RAPID：公众对 COVID-19 的风险认知和心理模型的演变

• 批准号: 2027094
• 负责人: Andrew Parker
• 金额: $ 19.97万
• 依托单位: Rand Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027094&HistoricalAwards=false
• 关键词: RAPID; Evolution; Public; Risk; Perception

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.

在诸如Covid-19之类的犯罪中，公众是重要的响应伙伴。新颖的威胁与公众有关，但通常会误解，导致不适当的反应。澄清何时以及为何发生误解很重要，因为导致的行为会导致疾病传播，供应短缺和不必要的医疗保健系统伯恩伯恩（Burnen）。中心是个人心理模型，由相关信念或个人有风险的感知组成的直观理论，这可能与科学共识可能不符。心理模型构成了人们如何构想风险，结构决策及其与风险相关的行为的基础。该项目遵循个人的风险看法，心理模型和风险行为，在Covid-19大流行过程中，利用了一个时间敏感的机会，以在具体的公共卫生环境中推动对犯罪的公共风险反应的发展。主要目标是纵向跟踪在COVID-19大流行过程中，个人内部的风险感知，心理模型和与风险相关的行为。次要目标是开发新的方法论方法来处理和分析大型心理模型数据，并吸引专家就我们的大型基础设施的方法和需求。该项目利用现有数据和计划的调查数据收集，建立了纵向评估，以便能够捕获风险感知，心理模型和行为的变化。调查使用FreeListing，这是一种人类学中简单的自由关联技术，以收集人们风险心理模型的大样本图片。研究团队员工自动化了词汇分析工具，以处理数据和网络分析技术以绘制心理模型。该团队使用回归分析来检查心理模型，风险感知，行为及其随着时间的变化之间的关系。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估来诚实地支持支持。