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- W4225567992 abstract "Now, in the time of COVID-19, such measures are even more important. If the promises held out for the universal acceptability and efficacy of ML and person-centric health care are to be realised, ML must be affordable at a grassroots level in LMICs. More collaboration and co-design involving mobile health developers, mobile telecoms companies, governments, trans-national organisations and non-governmental organisations (NGOs) could result in affordable generic open-source templates. Most importantly, such a collaboration needs to be done in parallel with paying CHWs a living wage. Achieving such a goal would remove the need for any CHW ever having to resort to prostitution to purchase medicines in order to treat their patients." @default.
- W4225567992 created "2022-05-05" @default.
- W4225567992 creator A5046771475 @default.
- W4225567992 date "2022-04-30" @default.
- W4225567992 modified "2023-10-16" @default.
- W4225567992 title "Machine learning in a time of COVID-19 – Can machine learning support Community Health Workers (CHWs) in low and middle income countries (LMICs) in the new normal?" @default.
- W4225567992 doi "https://doi.org/10.31219/osf.io/eknj9" @default.
- W4225567992 hasPublicationYear "2022" @default.
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