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

dc.contributor.authorRiad, Abanoub
dc.contributor.authorHuang, Yi
dc.contributor.authorAbdulqader, Huthaifa
dc.contributor.authorMorgado, Mariana
dc.contributor.authorDomnori, Silvi
dc.contributor.authorKošˇcík, Michal
dc.contributor.authorMendes, JoséJoão
dc.contributor.authorKlugar, Miloslav
dc.contributor.authorKateeb, Elham
dc.description.abstractBackground: 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.en_US
dc.identifier.citationRiad, A.; Huang, Y.; Abdulqader, H.; Morgado, M.; Domnori, S.; Košˇcík, M.; Mendes, J.J.; Klugar, M.; Kateeb, E.; IADS-SCORE. Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machineen_US
dc.publisherMDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations.en_US
dc.subjectCOVID-19 vaccinesen_US
dc.subjectdecision makingen_US
dc.subjectdecision treesen_US
dc.subjectdental educationen_US
dc.subjectinternational association of dental studentsen_US
dc.subjectmachine learningen_US
dc.subjectmass vaccinationen_US
dc.subjectregression analysisen_US
dc.titleUniversal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approachen_US
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