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

RCTs - Protocols/Proposals (eHealth) (398) Chatbots and Conversational Agents (188) mHealth for Wellness, Behavior Change and Prevention (2277) Text-messaging (SMS, WeChat etc)-Based Interventions (528) Depression and Mood Disorders; Suicide Prevention (1032) Perinatal Depression; Postpartum Depression; PPD (72) Perinatal Education for Parents (160)

Published on in Vol 8 , No 4 (2019) :April

Expanding Access to Depression Treatment in Kenya Through Automated Psychological Support: Protocol for a Single-Case Experimental Design Pilot Study

Expanding Access to Depression Treatment in Kenya Through Automated Psychological Support: Protocol for a Single-Case Experimental Design Pilot Study

Expanding Access to Depression Treatment in Kenya Through Automated Psychological Support: Protocol for a Single-Case Experimental Design Pilot Study

Authors of this article:

Eric P Green 1 Author Orcid Image ;   Nicholas Pearson 1, 2 Author Orcid Image ;   Sathyanath Rajasekharan 3 Author Orcid Image ;   Michiel Rauws 4 Author Orcid Image ;   Angela Joerin 4 Author Orcid Image ;   Edith Kwobah 5 Author Orcid Image ;   Christine Musyimi 6 Author Orcid Image ;   Chaya Bhat 1 Author Orcid Image ;   Rachel M Jones 3 Author Orcid Image ;   Yihuan Lai 1 Author Orcid Image
Article Authors Cited by (11) Tweetations (13) Metrics

Journals

  1. Hussain-Shamsy N, Shah A, Vigod S, Zaheer J, Seto E. Mobile Health for Perinatal Depression and Anxiety: Scoping Review. Journal of Medical Internet Research 2020;22(4):e17011 View
  2. Kohrt B, Ottman K, Panter-Brick C, Konner M, Patel V. Why we heal: The evolution of psychological healing and implications for global mental health. Clinical Psychology Review 2020;82:101920 View
  3. Rahman A, Waqas A, Nisar A, Nazir H, Sikander S, Atif N. Improving access to psychosocial interventions for perinatal depression in low- and middle-income countries: lessons from the field. International Review of Psychiatry 2021;33(1-2):198 View
  4. Davidson L, Boland M. Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes. Briefings in Bioinformatics 2021;22(5) View
  5. Dosani A, Arora H, Mazmudar S. mHealth and Perinatal Depression in Low-and Middle-Income Countries: A Scoping Review of the Literature. International Journal of Environmental Research and Public Health 2020;17(20):7679 View
  6. Green E, Lai Y, Pearson N, Rajasekharan S, Rauws M, Joerin A, Kwobah E, Musyimi C, Jones R, Bhat C, Mulinge A, Puffer E. Expanding Access to Perinatal Depression Treatment in Kenya Through Automated Psychological Support: Development and Usability Study. JMIR Formative Research 2020;4(10):e17895 View
  7. Oprescu A, Miro-Amarante G, Garcia-Diaz L, Beltran L, Rey V, Romero-Ternero M. Artificial Intelligence in Pregnancy: A Scoping Review. IEEE Access 2020;8:181450 View
  8. Jellinek M, Bergmann P, Holcomb J, Riobueno-Naylor A, Dutta A, Haile H, Sturner R, Howard B, Murphy J. Recognizing Adolescent Depression with Parent- and Youth-Report Screens in Pediatric Primary Care. The Journal of Pediatrics 2021;233:220 View
  9. Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Frontiers in Endocrinology 2023;14 View

Books/Policy Documents

  1. Mattioli M. Governing Privacy in Knowledge Commons. View
  2. Waqas A, Rahman A. Shaping the Future of Child and Adolescent Mental Health. View