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 learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes.

Participatory approaches, local stakeholders and cultural relevance facilitate an impactful community-based project in Uganda.

Sanitation is a major global challenge that is often addressed at national and international levels, while community opinions and beliefs are neglected. To promote water, sanitation and hygiene (WASH) we organized a cross-cultural knowledge exchange workshop to assess participatory methods for engaging local stakeholders. The workshop included 22 participants from all sectors of society.