The impact of COVID-19 on routine child immunisation in South Africa.

Background: The COVID-19 pandemic disrupted immunisation programs worldwide, reversing gains that had brought vaccine-preventable diseases largely under control. This study explored the impact of COVID-19 on the uptake of routine child immunisation services in South Africa.

Methods: We conducted qualitative research using in-depth interviews with 51 purposively selected parents/caregivers of children below the age of five who missed or delayed one or more scheduled immunisation doses in 2020-2022 and with 12 healthcare providers who provided public immunisation services during the pandemic.

Community perceived barriers to uptake of health services among men at Sedibeng district in South Africa.

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 Health Solutions' Facebook page for two weeks. A convenience sampling method was used and a total of 104 comments were collected from 64 respondents.

Understanding health outcome drivers among adherence club patients in clinics of Gauteng, South Africa: a structural equation modelling (SEM) approach.

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 experiencing positive or negative health outcomes. Human Immunodeficiency Virus (HIV) continues to be a significant global public health challenge, with an estimated 38 million people living with the aim of this study was therefore to develop and empirically test a conceptual research model using SEM, aimed at explaining the magnitude of various factors influencing HIV and other health outcomes among patients attending Adherence Clubs.

Application of machine learning models in predicting length of stay among healthcare workers in underserved communities in South Africa.

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 related to general health practitioners' decision to stay in public health practice, recruitment agencies have no validated methods to predict how long these health workers will commit to their placement. We aim to use machine learning methods to predict health professional's length of practice in the rural public healthcare sector based on their demographic information.