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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F21%3A00122571" target="_blank" >RIV/00216224:14110/21:00122571 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-393X/9/10/1158" target="_blank" >https://www.mdpi.com/2076-393X/9/10/1158</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/vaccines9101158" target="_blank" >10.3390/vaccines9101158</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30102 - Immunology

Project

<a href="/en/project/LTC20031" target="_blank" >LTC20031: Towards an International Network for Evidence-based Research in Clinical Health Research in the Czech Republic</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Vaccines

ISSN

2076-393X

e-ISSN

2076-393X

Volume of the periodical

9

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

1-19

UT code for WoS article

000726871300001

EID of the result in the Scopus database

2-s2.0-85117531846