This paper is in the following e-collection/theme issue:

COVID-19 Vaccination (146) JMIR Theme Issue: COVID-19 Special Issue (2452) Infectious Diseases (non-STD/STI) (1575) Cross-Sectional Studies in Public Health (278) Vaccination and Immunization in the Digital Age (374) Artificial Intelligence (1371) Machine Learning (1465) Machine Learning (49)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47979, first published .
Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Authors of this article:

Nicole L Vike1 Author Orcid Image ;   Sumra Bari1 Author Orcid Image ;   Leandros Stefanopoulos2, 3 Author Orcid Image ;   Shamal Lalvani2 Author Orcid Image ;   Byoung Woo Kim1 Author Orcid Image ;   Nicos Maglaveras3 Author Orcid Image ;   Martin Block4 Author Orcid Image ;   Hans C Breiter1, 5 Author Orcid Image ;   Aggelos K Katsaggelos2, 6, 7 Author Orcid Image
Article Authors Cited by (2) Tweetations (7) Metrics

Journals

  1. Lalvani S, Bari S, Vike N, Stefanopoulos L, Kim B, Block M, Maglaveras N, Katsaggelos A, Breiter H. Predicting suicidality with small sets of interpretable reward behavior and survey variables. Nature Mental Health 2024;2(7):773 View
  2. Bari S, Kim B, Vike N, Lalvani S, Stefanopoulos L, Maglaveras N, Block M, Strawn J, Katsaggelos A, Breiter H. A novel approach to anxiety level prediction using small sets of judgment and survey variables. npj Mental Health Research 2024;3(1) View