Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app

Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app

Background: Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine...

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Bibliographic Details
Main Authors: Geniets, A. (Author), Hamala, R. (Author), Kabali, K. (Author), Kahn, K. (Author), Lakati, A. (Author), MacRae, M.K (Author), Mbae, S.M (Author), Namanda, A.S (Author), O’Donovan, J. (Author), Winters, N. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
access to information
Access to Information
artificial intelligence
Artificial intelligence
Artificial Intelligence
Community Health Worker
Community Health Workers
Digital Health
education
health auxiliary
human
Humans
Kenya
machine learning
Machine learning
Machine Learning
mobile application
Mobile Applications
reproducibility
Reproducibility of Results
Supervision
Training
Uganda
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