3 research outputs found
Developing indicators for Monitoring and evaluation of the implementation of the Primary Health Care Approach in Health Sciences at the University of Cape Town using a DELPHI method
No full textBackground The University of Cape Town Faculty of Health Sciences (UCT FHS) adopted the Primary Health Care (PHC) approach as its lead theme for teaching, research, and clinical service in1994 Aim To develop indicators to monitor and evaluate the implementation of the PHC approach in Health Sciences Education . Method A Delphi study, conducted over two rounds, presented indicators of Social Accountability from the Training for Health Equity Network (THEnet), as well as indicators derived from the principles of the PHC approach in the UCT FHS, to a national multidisciplinary panel. An electronic questionnaire was used to score each indicator according to relevance, feasibility/measurability, and its application to undergraduate and postgraduate curricula. Qualitative feedback on the proposed indicators was also elicited. Results Round 1: Of the 59 Social Accountability indicators presented to the panel, the 20 highest ranked indicators were selected for Round 2. Qualitative feedback challenged the link between social accountability and PHC, resulting in an additional 19 PHC-specific indicators being presented in Round 2. Round 2: The indicators which scored >85% and made the final list were: PHC: Continuity of care (94%); Holistic understanding of health care (88%); Respecting human rights (88%); Providing accessible care to all (88%); and Promoting health through health education (88%). THEnet: Safety of learners (88%); Education reflects communities' needs (86%); Teaching embodies social accountability (86%); Teaching is appropriate to learners' needs (86%) Conclusion These PHC and THEnet indicators can be used to assess the implementation of PHC in Health Sciences Education. The specific indicators identified reflect priorities relevant to the local context. One limitation is that some key priority indicators did not make the final list
Associations with virologic treatment failure in adults on antiretroviral therapy in South Africa.
No full textOBJECTIVES: Highly active antiretroviral therapy (HAART) has been available in government facilities in the Western Cape Province of South Africa since 2001. We aimed to investigate factors associated with virologic treatment failure in this setting. DESIGN: Case-control study, matched on facility and on starting date and duration of HAART. METHODS: Cases and controls were identified from clinic registers from May 2001 to June 2006. Cases were patients who switched to second-line therapy after confirmed virologic failure (2 consecutive viral loads above 1000 copies/mL). Controls were on first-line treatment with viral load <400 copies per milliliter at the time of case incidence. RESULTS: One hundred thirty cases and 238 controls were selected from 8 clinics (median 16.6 months on HAART, interquartile range: 12.2-24.6). Treatment interruptions [adjusted odds ratio (AOR) 8.6, 95% confidence interval: 3.6 to 20.8], prior nevirapine-based prevention of mother-to-child transmission (PMTCT) treatment (AOR: 9.6, 95% confidence interval: 2.9 to 32.2), a baseline CD4 count less than 50 cells per microliter or from 50-150 cells per microliter (AOR: 6.6, 95% confidence interval: 2.3 to 18.8 and AOR: 5.8, 95% confidence interval: 2.1 to 16.3 compared with a baseline CD4 count of more than 150 cells/microL), and the use of nevirapine in the initial regimen (AOR: 2.5, 95% confidence interval: 1.4 to 4.7) were all independently associated with virologic treatment failure. CONCLUSIONS: In this setting, nevirapine in the initial HAART regimen or for PMTCT treatment is associated with virologic treatment failure, together with low CD4 count at ART initiation. Earlier initiation of HAART and access to improved triple therapy and PMTCT regimens are priorities for HIV programs in Southern Africa
Benchmarking IsiXhosa Automatic Speech Recognition and Machine Translation for Digital Health Provision
No full textAs digital health becomes more ubiquitous, people from different geographic regions are connected and there is thus a need for accurate language translation services. South Africa presents opportunity and need for digital health innovation, but implementing indigenous translation systems for digital health is difficult due to a lack of language resources. Understanding the accuracy of current models for use in medical translation of indigenous languages is crucial for designers looking to build quality digital health solutions. This paper presents a new dataset with audio and text of primary health consultations for automatic speech recognition and machine translation in South African English and the indigenous South African language of isiXhosa. We then evaluate the performance of well-established pretrained models on this dataset. We found that isiXhosa had limited support in speech recognition models and showed high, variable character error rates for transcription (26-70%). For translation tasks, Google Cloud Translate and ChatGPT outperformed the other evaluated models, indicating large language models can have similar performance to dedicated machine translation models for low-resource language translation
