Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
Date
2021-10-10
Authors
Riad, Abanoub
Huang, Yi
Abdulqader, Huthaifa
Morgado, Mariana
Domnori, Silvi
Košˇcík, Michal
Mendes, JoséJoão
Klugar, Miloslav
Kateeb, Elham
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Journal Title
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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.
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Keywords
COVID-19 vaccines , decision making , decision trees , dental education , international association of dental students , machine learning , mass vaccination , regression analysis
Citation
Riad, 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: Machine