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- ItemPartnerships in a global mental health research programme — the example of PRIME(Springer, 2019) Breuer, Erica; Hanlon, Charlotte; Bhana, Arvin; Chisholm, Dan; De Silva, Mary; Fekadu, Abebaw; Honikman, Simone; Jordans, Mark; Kathree, Tasneem; Kigozi, Fred; Luitel, Nagendra P.; Marx, Maggie; Medhin, Girmay; Murhar, Vaibhav; Ndyanabangi, Sheila; Patel, Vikram; Petersen, Inge; Prince, Martin; Raja, Shoba; Rathod, Sujit D.; Shidhaye, Rahul; Ssebunnya, Joshua; Thornicroft, Graham; Tomlinson, Mark; Wolde-Giorgis, Tedla; Lund, CrickCollaborative research partnerships are necessary to answer key questions in global mental health, to share expertise, access funding and influence policy. However, partnerships between low- and middle-income countries (LMIC) and high-income countries have often been inequitable with the provision of technical knowledge flowing unilaterally from high to lower income countries. We present the experience of the Programme for Improving Mental Health Care (PRIME), a LMIC-led partnership which provides research evidence for the development, implementation and scaling up of integrated district mental healthcare plans in Ethiopia, India, Nepal, South Africa and Uganda. We use Tuckman’s first four stages of forming, storming, norming and performing to reflect on the history, formation and challenges of the PRIME Consortium. We show how this resulted in successful partnerships in relation to management, research, research uptake and capacity building and reflect on the key lessons for future partnerships.
- ItemIntrapartum HIV transmission rate in a central hospital in the Western Cape Province after universal antiretroviral therapy roll-out(AOSIS publishers, 2020-12) Van der Merwe, Tian A.; Van Zyl, Gert U.; Lombards, Carl J.; Theron, Gerhard B.
- ItemObservations on glucose excursions with the use of a simple protocol for insulin, following antenatal betamethasone administration(Frontiers Media, 2020-12) Paulsen, Chane; Hall, David R.
- ItemTechnology-conversant management education : introducing a new discipline(Southern African Institute for Industrial Engineering, 2014-05) Van Wyk, Rias JohannIn the first quarter of 2013, the Department of Industrial Engineering at the University of Stellenbosch launched a new academic course, Strategic Technology Analysis (STA), as an elective in its M.Sc. in Engineering Management and M.Eng. Industrial Engineering degrees. STA views technology as a knowledge area in its own right, focuses on the inherent characteristics of technology, and explores its natural order. The purpose was to ascertain whether a course of this nature, which offered the outline for a new academic discipline, would be of benefit to a technology-conversant management programme. The course was well-received. It encouraged a greater awareness of technological positioning — i.e., aligning overall corporate strategy with new opportunities across the entire technological frontier. This article describes the background to this initiative, the history of STA, its inherent structure, and its role in professional practice. It then looks ahead at the possible dissemination of this knowledge into different settings where technology-conversant management is taught.
- ItemUsing thermal transients at the outlet of electrical water heaters to recognise consumption patterns for heating schedule optimisation(IEEE - Institute of Electrical and Electronics Engineers, 2015-07) Nel, P. J. C.; Booysen, Marthinus J.; Van Der Merwe, B.In the midst of environmental concerns, and soaring energy costs and energy shortages, the efficiency of electrical household water heaters (EWHs) has been identified as an area with significant potential for savings. The benefits of applying optimised scheduling control for EWHs has been proven by various studies, however, little has been done to measure individual behaviour. This paper presents an alternative to the invasive and expensive solution of using water flow meters. A hardware and algorithmic solution is presented that uses thermal transients at the outlet of an EWH to measure consumption patterns. The results show that the approach is able to detect usage events with an accuracy of 91%. Despite the challenges related to thermal inaccuracies, event durations are estimated to within 2 minutes accuracy 79% of the time.