Published on in Vol 5, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38063, first published .
Understanding Whole-Person Health and Resilience During the COVID-19 Pandemic and Beyond: A Cross-sectional and Descriptive Correlation Study

Understanding Whole-Person Health and Resilience During the COVID-19 Pandemic and Beyond: A Cross-sectional and Descriptive Correlation Study

Understanding Whole-Person Health and Resilience During the COVID-19 Pandemic and Beyond: A Cross-sectional and Descriptive Correlation Study

Journals

  1. Pirsch A, Austin R, Martin L, Pieczkiewicz D, Monsen K. Using data visualization to characterize whole‐person health of public health nurses. Public Health Nursing 2023;40(5):612 View
  2. Austin R, McLane T, Pieczkiewicz D, Adam T, Monsen K. Advantages and disadvantages of using theory-based versus data-driven models with social and behavioral determinants of health data. Journal of the American Medical Informatics Association 2023;30(11):1818 View
  3. Austin R, Rajamani S, Jantraporn R, Pirsch A, Martin K. Examining standardized consumer-generated social determinants of health and resilience data supported by Omaha System terminology. Journal of the American Medical Informatics Association 2023;30(11):1852 View
  4. Aldekhyyel R, Alshuaibi F, Alsaaid O, Bin Moammar F, Alanazy T, Namshah A, Altassan K, Aldekhyyel R, Jamal A. Exploring behavioral intention to use telemedicine services post COVID-19: a cross sectional study in Saudi Arabia. Frontiers in Public Health 2024;12 View
  5. Huling J, Austin R, Lu S, Mathiason M, Pirsch A, Monsen K. Comparison of Weighting Methods to Understand Improved Outcomes Attributable to Public Health Nursing Interventions. Nursing Research 2024;73(5):390 View
  6. Austin R, Jantraporn R, Michalowski M, Marquard J. Machine learning methods to discover hidden patterns in well‐being and resilience for healthy aging. Journal of Nursing Scholarship 2024 View