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Article

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

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Department of Public Health, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic
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International Association of Dental Students (IADS), 1216 Geneva, Switzerland
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Czech National Centre for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech EBHC: JBI Centre of Excellence, Masaryk University GRADE Centre), Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
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Department of Psychology, Faculty of Social Studies, Masaryk University, 602 00 Brno, Czech Republic
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Institute for Research of Children, Youth and Family, Faculty of Social Studies, Masaryk University, 602 00 Brno, Czech Republic
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Clinical Research Unit (CRU), Egas Moniz Cooperativa de Ensino Superior, 2829-511 Almada, Portugal
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Oral Health Research and Promotion Unit, Faculty of Dentistry, Al-Quds University, Jerusalem 510 00, Palestine
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Public Health Committee, World Dental Federation (FDI), 1216 Geneva, Switzerland
*
Author to whom correspondence should be addressed.
IADS-SCORE stands for the Standing Committee on Research and Education (SCORE) of the International Association of Dental Students (IADS). The consortium members of IADS-SCORE are listed in Appendix A.
Academic Editor: Yutaka Ueda
Vaccines 2021, 9(10), 1158; https://doi.org/10.3390/vaccines9101158
Received: 9 September 2021 / Revised: 1 October 2021 / Accepted: 8 October 2021 / Published: 10 October 2021
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. View Full-Text
Keywords: COVID-19 vaccines; decision making; decision trees; dental education; international association of dental students; machine learning; mass vaccination; regression analysis COVID-19 vaccines; decision making; decision trees; dental education; international association of dental students; machine learning; mass vaccination; regression analysis
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MDPI and ACS Style

Riad, A.; Huang, Y.; Abdulqader, H.; Morgado, M.; Domnori, S.; Koščík, M.; Mendes, J.J.; Klugar, M.; Kateeb, E.; IADS-SCORE. Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach. Vaccines 2021, 9, 1158. https://doi.org/10.3390/vaccines9101158

AMA Style

Riad A, Huang Y, Abdulqader H, Morgado M, Domnori S, Koščík M, Mendes JJ, Klugar M, Kateeb E, IADS-SCORE. Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach. Vaccines. 2021; 9(10):1158. https://doi.org/10.3390/vaccines9101158

Chicago/Turabian Style

Riad, Abanoub, Yi Huang, Huthaifa Abdulqader, Mariana Morgado, Silvi Domnori, Michal Koščík, José J. Mendes, Miloslav Klugar, Elham Kateeb, and IADS-SCORE. 2021. "Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach" Vaccines 9, no. 10: 1158. https://doi.org/10.3390/vaccines9101158

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