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

Reviews on Usage or other Formative Evaluation Metrics (44) JMIR Theme Issue: COVID-19 Special Issue (2452) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Outbreak and Pandemic Preparedness and Management (1727) Infectious Diseases (non-STD/STI) (1575) Natural Language Processing (692) Artificial Intelligence (1371) Depression and Mood Disorders; Suicide Prevention (1363)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33637, first published .
Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis

Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis

Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis

Authors of this article:

Ruby Castilla-Puentes1, 2, 3 Author Orcid Image ;   Jacqueline Pesa4 Author Orcid Image ;   Caroline Brethenoux5 Author Orcid Image ;   Patrick Furey5 Author Orcid Image ;   Liliana Gil Valletta5 Author Orcid Image ;   Tatiana Falcone6 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Han B, Guan H. Associations between new health conditions and healthcare service utilizations among older adults in the United Kingdom: effects of COVID-19 risks, worse financial situation, and lowered income. BMC Geriatrics 2022;22(1) View
  2. Lee E, Agustines D, Woo B. Selection Bias in Digital Conversations on Depression Before and During COVID-19. JMIR Formative Research 2023;7:e42545 View
  3. Shi W, Donovan E, Quaack K, Mackert M, Shaffer A, De Luca D, Nolan-Cody H, Yang J. A Reasoned Action Approach to Social Connection and Mental Health: Racial Group Differences and Similarities in Attitudes, Norms, and Intentions. Health Communication 2024;39(11):2197 View
  4. Anderson A, Pesa J, Choudhry Z, Brethenoux C, Furey P, Jackson L, Valleta L, Quijano L, Lorenzo A. Patient perceptions of disease burden and treatment of myasthenia gravis based on sentiment analysis of digital conversations. Scientific Reports 2024;14(1) View