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@article{1797497,
author = {Riad, Abanoub and Huang, Yi and Abdulqader, Huthaifa and Morgado, Mariana and Domnori, Silvi and Koščík, Michal and Mendes, José João and Klugar, Miloslav and Kateeb, Elham},
article_location = {Basel},
article_number = {10},
doi = {http://dx.doi.org/10.3390/vaccines9101158},
keywords = {COVID-19 vaccines; decision making; decision trees; dental education; international association of dental students; machine learning; mass vaccination; regression analysis},
language = {eng},
issn = {2076-393X},
journal = {Vaccines},
title = {Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach},
url = {https://www.mdpi.com/2076-393X/9/10/1158},
volume = {9},
year = {2021}
}
TY - JOUR
ID - 1797497
AU - Riad, Abanoub - Huang, Yi - Abdulqader, Huthaifa - Morgado, Mariana - Domnori, Silvi - Koščík, Michal - Mendes, José João - Klugar, Miloslav - Kateeb, Elham
PY - 2021
TI - Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
JF - Vaccines
VL - 9
IS - 10
SP - 1-19
EP - 1-19
PB - MDPI
SN - 2076393X
KW - COVID-19 vaccines
KW - decision making
KW - decision trees
KW - dental education
KW - international association of dental students
KW - machine learning
KW - mass vaccination
KW - regression analysis
UR - https://www.mdpi.com/2076-393X/9/10/1158
N2 - 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.
ER -
RIAD, Abanoub, Yi HUANG, Huthaifa ABDULQADER, Mariana MORGADO, Silvi DOMNORI, Michal KOŠČÍK, José João MENDES, Miloslav KLUGAR a Elham KATEEB. Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach. \textit{Vaccines}. Basel: MDPI, roč.~9, č.~10, s.~1-19. ISSN~2076-393X. doi:10.3390/vaccines9101158. 2021.
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