Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Behavioral Sciences |
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| Online Access: | https://www.mdpi.com/2076-328X/14/3/217 |
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| author | Samaneh Omranian Alireza Khoddam Celeste Campos-Castillo Sajjad Fouladvand Susan McRoy Janet Rich-Edwards |
| author_facet | Samaneh Omranian Alireza Khoddam Celeste Campos-Castillo Sajjad Fouladvand Susan McRoy Janet Rich-Edwards |
| author_sort | Samaneh Omranian |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-24T18:33:08Z |
| format | Article |
| id | doaj.art-2ba026a19b0a44899e5f75d838c0df3f |
| institution | Directory Open Access Journal |
| issn | 2076-328X |
| language | English |
| last_indexed | 2024-04-24T18:33:08Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj.art-2ba026a19b0a44899e5f75d838c0df3f2024-03-27T13:21:32ZengMDPI AGBehavioral Sciences2076-328X2024-03-0114321710.3390/bs14030217Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US NursesSamaneh Omranian0Alireza Khoddam1Celeste Campos-Castillo2Sajjad Fouladvand3Susan McRoy4Janet Rich-Edwards5Division of Women’s Health, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USADivision of Women’s Health, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USADepartment of Media and Information, Michigan State University, East Lansing, MI 48824, USAStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USADepartment of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USADivision of Women’s Health, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USAWe 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.https://www.mdpi.com/2076-328X/14/3/217COVID-19 vaccinationhealthcare providersNurses’ Health Studyvaccine hesitancyhealth belief modelartificial intelligence |
| spellingShingle | Samaneh Omranian Alireza Khoddam Celeste Campos-Castillo Sajjad Fouladvand Susan McRoy Janet Rich-Edwards Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses Behavioral Sciences COVID-19 vaccination healthcare providers Nurses’ Health Study vaccine hesitancy health belief model artificial intelligence |
| title | Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses |
| title_full | Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses |
| title_fullStr | Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses |
| title_full_unstemmed | Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses |
| title_short | Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses |
| title_sort | leveraging artificial intelligence to predict health belief model and covid 19 vaccine uptake using survey text from us nurses |
| topic | COVID-19 vaccination healthcare providers Nurses’ Health Study vaccine hesitancy health belief model artificial intelligence |
| url | https://www.mdpi.com/2076-328X/14/3/217 |
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