Published on in Vol 24, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38359, first published .
Co-Development of a Web Application (COVID-19 Social Site) for Long-Term Care Workers (“Something for Us”): User-Centered Design and Participatory Research Study

Co-Development of a Web Application (COVID-19 Social Site) for Long-Term Care Workers (“Something for Us”): User-Centered Design and Participatory Research Study

Co-Development of a Web Application (COVID-19 Social Site) for Long-Term Care Workers (“Something for Us”): User-Centered Design and Participatory Research Study

Journals

  1. Stevens G, Johnson L, Saunders C, Schmidt P, Sierpe A, Thomeer R, Little N, Cantrell M, Yen R, Pogue J, Holahan T, Schubbe D, Forcino R, Fillbrook B, Sheppard R, Wooten C, Goldmann D, O’Malley A, Dubé E, Durand M, Elwyn G. The CONFIDENT study protocol: a randomized controlled trial comparing two methods to increase long-term care worker confidence in the COVID-19 vaccines. BMC Public Health 2023;23(1) View
  2. Zhou C, Huang T, Luo X, Kaner J. Reorganisation and Construction of an Age-Friendly Smart Recreational Home System: Based on Function–Capability Match Methodology. Applied Sciences 2023;13(17):9783 View
  3. Wexler C, Dixon K, Oyowe K, Lapke B, Conner H, Shoemaker H, Corriveau E, Greiner A, Finocchario-Kessler S. Development and evaluation of a COVID tracking system to support provision of social service in Wyandotte County, Kansas. Frontiers in Public Health 2023;11 View
  4. Saunders C, Sierpe A, von Plessen C, Kennedy A, Leviton L, Bernstein S, Goldwag J, King J, Marx C, Pogue J, Saunders R, Van Citters A, Yen R, Elwyn G, Leyenaar J. Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis. BMJ 2023:e074256 View
  5. Chan H, Wang C, Jeng W, Huang Y. Strengthening scientific credibility in the face of misinformation and disinformation: Viable solutions. Journal of Controlled Release 2023;360:163 View
  6. Yasin P, Yimit Y, Cai X, Aimaiti A, Sheng W, Mamat M, Nijiati M. Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI). European Journal of Medical Research 2024;29(1) View