Fonn S: Health in South Africa 2, saving the lives of South Africa’s mothers, babies and children: can the health system deliver? Lancet 2009
Bibliographic record
Abstract
South Africa is one of only 12 countries in which mortality rates for children have increased since the baseline for the Millennium Development Goals (MDGs) in 1990. Continuing poverty and the HIV/AIDS epidemic are important factors. Additionally, suboptimum implementation of high-impact interventions limits programme eff ectiveness; between a quarter and half of maternal, neonatal, and child deaths in national audits have an avoidable health-system factor contributing to the death. Using the LiST model, we estimate that 11 500 infants ’ lives could be saved by eff ective implementation of basic neonatal care at 95 % coverage. Similar coverage of dual-therapy prevention of mother-to-child transmission with appropriate feeding choices could save 37 200 children’s lives in South Africa per year in 2015 compared with 2008. These interventions would also avert many maternal deaths and stillbirths. The total cost of such a target package is US$1·5 billion per year, 24 % of the public-sector health expenditure; the incremental cost is $220 million per year. Such progress would put South Africa squarely on track to meet MDG 4 and probably also MDG 5. The costs are aff ordable and the key gap is leadership and eff ective implementation at every level of the health system, including national and local accountability for service provision.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.006 |
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".