RAPID: Evolution of Public Risk Perception and Mental Models Regarding COVID-19
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2027094
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$199,717Funder
National Science Foundation (NSF)Principal Investigator
Andrew ParkerResearch Location
United States of AmericaLead Research Institution
Rand CorporationResearch Priority Alignment
N/A
Research Category
Secondary impacts of disease, response & control measures
Research Subcategory
Social impacts
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
Social, Behavioral and Economic Sciences - 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.
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.