Evaluation of antiretroviral treatment programme monitoring in Eastern Cape, South Africa
Bibliographic record
Abstract
INTRODUCTION: The provision of antiretroviral treatment (ART) for HIV infection is a key strategy in addressing the high burden of HIV/AIDS in South Africa and improving the quality and length of life for those infected. Information produced from routine monitoring is essential for evidence-based decision-making within ART programmes. An evaluation of the ART programme data system in Eastern Cape, South Africa was conducted to determine the causes of irregular reporting and to make recommendations to improve data quality. METHODS: Data audits and semi-structured interviews were performed in facilities that initiate and provide ART. Thirty-two facilities in three sub-districts were audited. RESULTS: The number of adults receiving ART was over-reported by 36.6% (P < 0.05) on the District Health Information System. The interviews of nurses and administrators revealed various factors that contributed to the inaccuracy of the data including training, staffing levels, use of registers, data verification processes, and standardization with programme partners. CONCLUSIONS: Recommendations to address the inaccuracy of ART programme data include improving knowledge translation during training of ART programme staff, ensuring the implementation of established data verification policies and procedures, rethinking the design of the programme to reduce the burden on health facilities and personnel, and standardizing information management procedures amongst the various governmental and non-governmental stakeholders. The challenges with reporting in the Eastern Cape may be shared by other South African provinces as well as other low-middle income countries that require high quality data to inform well-designed and well-implemented interventions in the fight against HIV/AIDS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".