Journal article
Universal Predictors of Dental Students' Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
Vaccines (Basel), Vol.9(10), p.1158
10/01/2021
DOI: 10.3390/vaccines9101158
PMCID: PMC8539257
PMID: 34696266
Abstract
Background: young adults represent a critical target for mass-vaccination strategies of COVID-19 that aim to achieve herd immunity. Healthcare students, including dental students, are perceived as the upper echelon of health literacy; therefore, their health-related beliefs, attitudes and behaviors influence their peers and communities. The main aim of this study was to synthesize a data-driven model for the predictors of COVID-19 vaccine willingness among dental students. Methods: a secondary analysis of data extracted from a recently conducted multi-center and multi-national cross-sectional study of dental students' attitudes towards COVID-19 vaccination in 22 countries was carried out utilizing decision tree and regression analyses. Based on previous literature, a proposed conceptual model was developed and tested through a machine learning approach to elicit factors related to dental students' willingness to get the COVID-19 vaccine. Results: machine learning analysis suggested five important predictors of COVID-19 vaccination willingness among dental students globally, i.e., the economic level of the country where the student lives and studies, the individual's trust of the pharmaceutical industry, the individual's misconception of natural immunity, the individual's belief of vaccines risk-benefit-ratio, and the individual's attitudes toward novel vaccines. Conclusions: according to the socio-ecological theory, the country's economic level was the only contextual predictor, while the rest were individual predictors. Future research is recommended to be designed in a longitudinal fashion to facilitate evaluating the proposed model. The interventions of controlling vaccine hesitancy among the youth population may benefit from improving their views of the risk-benefit ratio of COVID-19 vaccines. Moreover, healthcare students, including dental students, will likely benefit from increasing their awareness of immunization and infectious diseases through curricular amendments.
Details
- Title: Subtitle
- Universal Predictors of Dental Students' Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
- Creators
- Abanoub Riad - Masaryk UniversityYi Huang - Masaryk UniversityHuthaifa Abdulqader - International Association for Dental ResearchMariana Morgado - International Association for Dental ResearchSilvi Domnori - International Association for Dental ResearchMichal Koscik - Masaryk UniversityJose Joao Mendes - Clinical Research Unit (CRU), Egas Moniz Cooperativa de Ensino Superior, 2829-511 Almada, Portugal.Miloslav Klugar - Masaryk UniversityElham Kateeb - Al-Quds UniversityIADS-SCORE (International Association of Dental Students-Standing Committee on Research and Education)
- Resource Type
- Journal article
- Publication Details
- Vaccines (Basel), Vol.9(10), p.1158
- DOI
- 10.3390/vaccines9101158
- PMID
- 34696266
- PMCID
- PMC8539257
- NLM abbreviation
- Vaccines (Basel)
- ISSN
- 2076-393X
- eISSN
- 2076-393X
- Publisher
- Mdpi
- Number of pages
- 19
- Grant note
- LTC20031 / INTEREXCELLENCE grant MUNI/IGA/1543/2020; MUNI/A/1608/2020 / Masaryk University
- Language
- English
- Date published
- 10/01/2021
- Academic Unit
- Public Policy Center (Archive)
- Record Identifier
- 9984283857702771
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