Published on in Vol 6 , No 12 (2018) :December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11468, first published .
Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

Journals

  1. Odendaal W, Anstey Watkins J, Leon N, Goudge J, Griffiths F, Tomlinson M, Daniels K. Health workers’ perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis. Cochrane Database of Systematic Reviews 2020 View
  2. Leonard E, de Kock I, Bam W. Barriers and facilitators to implementing evidence-based health innovations in low- and middle-income countries: A systematic literature review. Evaluation and Program Planning 2020;82:101832 View
  3. Van Belle S. Global Health Research, Anthropology and Realist Enquiry. Anthropology in Action 2019;26(1):42 View
  4. Marchal B, Abejirinde I, Sulaberidze L, Chikovani I, Uchaneishvili M, Shengelia N, Diaconu K, Vassall A, Zoidze A, Giralt A, Witter S. How do participatory methods shape policy? Applying a realist approach to the formulation of a new tuberculosis policy in Georgia. BMJ Open 2021;11(6):e047948 View
  5. Yoo N, Jang S. Digital technology use, technological self-efficacy, and subjective well-being among North Korean migrants during the COVID-19 pandemic: Moderated moderation. DIGITAL HEALTH 2023;9:205520762311715 View

Books/Policy Documents

  1. Gambo I, Ayegbusi E, Abioye O, Omodunbi T, Ikono R, Olufokunbi K. Advancing Health Education With Telemedicine. View