Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Sangiwe Moyo"'
Autor:
Ndumiso Tshuma, Daniel N. Elakpa, Clinton Moyo, Tshepo M. Ndhlovu, Mathildah M. Mokgatle, Sangiwe Moyo, Sehlule Moyo, Martha Chadyiwa, Mandeep K. Kochar, Mokgadi Malahlela, Takalani G. Tshitangano, David D. Mphuthi
Publikováno v:
Health SA Gesondheid: Journal of Interdisciplinary Health Sciences, Vol 29, Iss 0, Pp e1-e9 (2024)
Background: This qualitative study aimed to investigate the barriers that hinder men’s utilisation of healthcare services in the Sedibeng district of South Africa. Methods: The study was conducted using flyers with questions posted on the Best Hea
Externí odkaz:
https://doaj.org/article/bb2f93e968c5431095b84c20e5cd7f4b
Autor:
Ndumiso Tshuma, Elakpa Daniel Ngbede, Tawanda Nyengerai, Oliver Mtapuri, Sangiwe Moyo, David D. Mphuthi, Peter Nyasulu
Publikováno v:
AIDS Research and Therapy, Vol 20, Iss 1, Pp 1-13 (2023)
Abstract Background There has been growing interest in understanding the drivers of health outcomes, both in developed and developing countries. The drivers of health outcomes, on the other hand, are the factors that influence the likelihood of exper
Externí odkaz:
https://doaj.org/article/b9e4a86ad026422d89b6bd7d387fc038
Publikováno v:
Human Resources for Health, Vol 16, Iss 1, Pp 1-9 (2018)
Abstract Background Human resource planning in healthcare can employ machine learning to effectively predict length of stay of recruited health workers who are stationed in rural areas. While prior studies have identified a number of demographic fact
Externí odkaz:
https://doaj.org/article/6439aa74d37347eeae022058cdcb0b9e
Autor:
Anil Audumbar Pise, Sangiwe Moyo
Publikováno v:
IoT in Healthcare Systems ISBN: 9781003145035
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::944bf258b2b0c99b03b7f4e0512fb770
https://doi.org/10.1201/9781003145035-9
https://doi.org/10.1201/9781003145035-9
Autor:
Sangiwe Moyo
Publikováno v:
TEXILA INTERNATIONAL JOURNAL OF PUBLIC HEALTH. 5:34-43
Publikováno v:
Human Resources for Health
Human Resources for Health, Vol 16, Iss 1, Pp 1-9 (2018)
Human Resources for Health, Vol 16, Iss 1, Pp 1-9 (2018)
Background Human resource planning in healthcare can employ machine learning to effectively predict length of stay of recruited health workers who are stationed in rural areas. While prior studies have identified a number of demographic factors relat