Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses.

Publication date: Mar 07, 2024

We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in Spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March-April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1970 comments from VH participants and trained two machine learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three health belief model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March-April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.

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Concepts Keywords
Algorithms artificial intelligence
Nurses COVID-19 vaccination
Pandemic health belief model
Vaccine healthcare providers
natural language processing
Nurses’ Health Study
text classification
vaccine hesitancy

Semantics

Type Source Name
disease VO COVID-19 vaccine
disease VO vaccine
disease IDO susceptibility
disease VO time
disease MESH allergic reactions
disease MESH COVID 19
disease VO USA
disease VO vaccination
disease MESH infection
disease MESH emergency
drug DRUGBANK Coenzyme M
disease VO vaccinated
disease VO unvaccinated
disease IDO process
disease VO volume
disease VO population
disease MESH overweight
disease MESH death
disease MESH morbidities
pathway REACTOME Vitamins
drug DRUGBANK Flunarizine
drug DRUGBANK Saquinavir
drug DRUGBANK Isoxaflutole
disease VO effectiveness
disease IDO algorithm
disease VO effective
disease VO pregnant women
disease MESH uncertainty
disease VO vaccine efficacy
disease VO protocol
disease IDO site
drug DRUGBANK Aspartame
disease VO immunization
drug DRUGBANK Guanosine
disease VO dose
drug DRUGBANK Etoperidone
disease MESH Chronic Diseases
disease MESH Influenza
disease VO efficient

Original Article

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