Human resource development and antiretroviral treatment in Free State province, South Africa
Bibliographic record
Abstract
BACKGROUND: In common with other developing countries, South Africa's public health system is characterised by human resource shortfalls. These are likely to be exacerbated by the escalating demand for HIV care and a large-scale antiretroviral therapy (ART) programme. Focusing on professional nurses, the main front-line providers of primary health care in South Africa, we studied patterns of planning, recruitment, training and task allocation associated with an expanding ART programme in the districts of one province, the Free State. METHODS: Data collection included an audit of professional nurse posts created and filled following the introduction of the ART programme, repeated surveys of facilities providing ART over two years to assess the deployment of staff, and secondary data analysis of government personnel databases to track broader patterns of recruitment and training. RESULTS: Although a substantial number of new professional nurse posts were established for the ART programme in the Free State, nearly 80% of these posts were filled by nurses transferring from other programmes within the same facility or from facilities within the same district, rather than by new recruits. From the beginning, ART nurse posts tended to be graded at a senior level, and later, in an effort to recruit professional nurses for the ART programme, the majority (54.6%) of nurses entering the programme were promoted to a senior level. The vacancy rate of nurse ART posts was significantly lower than that of other posts in the primary health care (PHC) system (15.7% vs 37.1%). Nursing posts in urban ART facilities were more easily filled than those in rural areas, exacerbating existing imbalances. The shift of nurses into the ART programme was partially compensated for by the appointment of additional support staff, task shifting to community health workers, and a large investment in training of PHC workers. However, the use of less-trained, mid-level enrolled nurses and nursing assistants in the ART programme remained low. CONCLUSION: The introduction of the ART programme has revealed both strengths and weaknesses of human resource development in one province of South Africa. Without concerted efforts to increase the supply of key health professionals, accompanied by changes in the deployment of health workers, the core goals of the ART programme--i.e. providing universal access to ART and strengthening the health system--will not be achieved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".