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

Details

• Personal Author:
  Campos-Castillo C ; Fouladvand S ; Khoddam A ; McRoy S ; Omranian S ; Rich-Edwards J
• Description:
  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. Description provided by NIOSH
• Subjects:
  • Cohort Studies
  • Communicable Diseases
  • Health Personnel
  • Occupational Health
  • Safety
• Keywords:
  • COVID-19
  • Nurses
  • Vaccines
  • Artificial intelligence
  • Longitudinal study
  • Cohort studies
  • Health care workers
  • Machine learning
  • Author Keywords: COVID-19 vaccination
  • healthcare providers
  More +
• ISSN:
  2076-328X
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  California ; Massachusetts ; Michigan ; OSHA Region 1 ; OSHA Region 5 ; OSHA Region 9 ; Wisconsin
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  14
• Issue:
  3
• NIOSHTIC Number:
  nn:20069485
• Citation:
  Behav Sci 2024 Mar; 14(3):217
• Contact Point Address:
  Samaneh Omranian, Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
• Email:
  omranian@uwm.edu
• Federal Fiscal Year:
  2024
• Performing Organization:
  Brigham and Women's Hospital
• Peer Reviewed:
  True
• Start Date:
  20200608
• Source Full Name:
  Behavioral Sciences
• Download URL:
  https://stacks.cdc.gov/view/cdc/207920/cdc_207920_DS1.pdf
• File Type:
  [PDF - 2.98 MB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0db946d135b297a589f95e56d3d7f62d39f5605e0e71a38b6ffd791b1a55abda19e318f3fe790f9a09278c6bf1b66bd7d4f7be31f42d8387a42b7473c39ceb68