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

J 2021

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

RIAD, Abanoub, Yi HUANG, Huthaifa ABDULQADER, Mariana MORGADO, Silvi DOMNORI et. al.

Základní údaje

Originální název

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

Autoři

RIAD, Abanoub (818 Egypt, garant, domácí), Yi HUANG (156 Čína, domácí), Huthaifa ABDULQADER, Mariana MORGADO, Silvi DOMNORI, Michal KOŠČÍK (203 Česká republika, domácí), José João MENDES, Miloslav KLUGAR (203 Česká republika, domácí) a Elham KATEEB

Vydání

Vaccines, Basel, MDPI, 2021, 2076-393X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30102 Immunology

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.961

Kód RIV

RIV/00216224:14110/21:00122571

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.3390/vaccines9101158

UT WoS

000726871300001

Klíčová slova anglicky

COVID-19 vaccines; decision making; decision trees; dental education; international association of dental students; machine learning; mass vaccination; regression analysis

Štítky

14110525, 14119612, 14119613, podil, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 1. 2. 2022 11:32, Mgr. Tereza Miškechová

Anotace

V originále

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.

Návaznosti

LTC20031, projekt VaV

Název: Towards an International Network for Evidence-based Research in Clinical Health Research in the Czech Republic

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Towards an International Network for Evidence-based Research in Clinical Health Research in the Czech Republic, INTER-COST

MUNI/A/1608/2020, interní kód MU

Název: Prohlubování znalostí v oblasti zdravotních rizik a benefitů výživy, prostředí a životního stylu III

Investor: Masarykova univerzita, Prohlubování znalostí v oblasti zdravotních rizik a benefitů výživy, prostředí a životního stylu III

MUNI/IGA/1543/2020, interní kód MU

Název: Evidence-based Practice of Healthcare Professionals and Students in the Czech Republic (Akronym: Evidence-Based Practice in Czechia)

Investor: Masarykova univerzita, Evidence-based Practice of Healthcare Professionals and Students in the Czech Republic

Citovat

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. Vaccines. Basel: MDPI, 2021, roč. 9, č. 10, s. 1-19. ISSN 2076-393X. Dostupné z: https://dx.doi.org/10.3390/vaccines9101158.

@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, 2021, roč.~9, č.~10, s.~1-19. ISSN~2076-393X. Dostupné z: https://dx.doi.org/10.3390/vaccines9101158.

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