Abanoub Riad|Yi Huang|Huthaifa Abdulqader|Mariana Morgado|Silvi Domnori|Michal Koš?ík|José João Mendes|Miloslav Klugar|Elham Kateeb|Iads-Score
Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
Masaryk University|Masaryk University|Masaryk University|Masaryk University|Al-Quds University|FDI World Dental Federation
background young adults represent critical target massvaccination strategies covid19 aim achieve herd immunity healthcare students including dental students perceived upper echelon health literacy therefore healthrelated beliefs attitudes behaviors influence peers communities main aim study synthesize datadriven model predictors covid19 vaccine willingness among dental students methods secondary analysis data extracted recently conducted multicenter multinational crosssectional study dental students attitudes towards covid19 vaccination 22 countries carried utilizing decision tree regression analyses based previous literature proposed conceptual model developed tested machine learning approach elicit factors related dental students willingness get covid19 vaccine results machine learning analysis suggested five important predictors covid19 vaccination willingness among dental students globally ie economic level country student lives studies individuals trust pharmaceutical industry individuals misconception natural immunity individuals belief vaccines riskbenefitratio individuals attitudes toward novel vaccines conclusions according socioecological theory countrys economic level contextual predictor rest individual predictors future research recommended designed longitudinal fashion facilitate evaluating proposed model interventions controlling vaccine hesitancy among youth population may benefit improving views riskbenefit ratio covid19 vaccines moreover healthcare students including dental students likely benefit increasing awareness immunization infectious diseases curricular amendments