Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47595, first published .
Predicting and Empowering Health for Generation Z by Comparing Health Information Seeking and Digital Health Literacy: Cross-Sectional Questionnaire Study

Predicting and Empowering Health for Generation Z by Comparing Health Information Seeking and Digital Health Literacy: Cross-Sectional Questionnaire Study

Predicting and Empowering Health for Generation Z by Comparing Health Information Seeking and Digital Health Literacy: Cross-Sectional Questionnaire Study

Journals

  1. Jiao W. Older Adults’ Exposure to Food Media Induced Unhealthy Eating during the COVID-19 Omicron Lockdown? Exploring Negative Emotions and Associated Literacy and Efficacy on Shanghainese. Foods 2024;13(12):1797 View
  2. Patel M, Villalobos F, Shan K, Tardo L, Horton L, Sguigna P, Blackburn K, Munoz S, Moog T, Smith A, Burgess K, McCreary M, Okuda D. Generative artificial intelligence versus clinicians: Who diagnoses multiple sclerosis faster and with greater accuracy?. Multiple Sclerosis and Related Disorders 2024;90:105791 View
  3. Xian X, Chang A, Xiang Y, Liu M. Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review. Interactive Journal of Medical Research 2024;13:e53672 View
  4. Băjenaru O, Băjenaru L, Ianculescu M, Constantin V, Gușatu A, Nuță C. Geriatric Healthcare Supported by Decision-Making Tools Integrated into Digital Health Solutions. Electronics 2024;13(17):3440 View
  5. Jiao W, Schulz P, Chang A. Addressing the role of eHealth literacy in shaping popular attitudes towards post-COVID-19 vaccination among Chinese adults. Humanities and Social Sciences Communications 2024;11(1) View
  6. Xu Y, Wang M, Bao L, Cheng Z, Li X. A cross-sectional study based on the Comprehensive Model of Information seeking: which factors influence health information-seeking behavior in patients with periodontitis. BMC Oral Health 2024;24(1) View