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

Area of Science:

• Public Health
• Vaccinology
• Health Behavior

Background:

• Young adults, particularly healthcare students, are crucial for COVID-19 vaccination strategies aiming for herd immunity.
• Dental students, as health literacy leaders, influence community health beliefs and behaviors.
• Understanding predictors of vaccine willingness in this demographic is vital.

Purpose of the Study:

• To develop a data-driven model identifying factors influencing COVID-19 vaccine willingness among dental students globally.
• To analyze predictors of vaccine acceptance within a key young adult demographic.

Main Methods:

• Secondary analysis of a multi-center, multi-national cross-sectional study involving dental students from 22 countries.
• Utilized decision tree and regression analyses, employing a machine learning approach to test a conceptual model.

Main Results:

• Identified five significant predictors of COVID-19 vaccine willingness: country economic level, trust in the pharmaceutical industry, misconception of natural immunity, belief in the vaccine risk-benefit ratio, and attitudes toward novel vaccines.
• Country economic level emerged as the sole contextual predictor, with others being individual-level factors.

Conclusions:

• Interventions to combat vaccine hesitancy should address individual perceptions of vaccine risk-benefit and awareness of immunization.
• Curricular enhancements focusing on immunization and infectious diseases are recommended for dental and healthcare students.
• Longitudinal research is advised to further validate the proposed predictive model.