Learning from success: the main drivers of the maternal and newborn health transition in seven positive-outlier countries and implications for future policies and programmes
Bibliographic record
Abstract
Currently, about 8% of deaths worldwide are maternal or neonatal deaths, or stillbirths. Maternal and neonatal mortality have been a focus of the Millenium Development Goals and the Sustainable Development Goals, and mortality levels have improved since the 1990s. We aim to answer two questions: What were the key drivers of maternal and neonatal mortality reductions seen in seven positive-outlier countries from 2000 to the present? How generalisable are the findings?We identified positive-outlier countries with respect to maternal and neonatal mortality reduction since 2000. We selected seven, and synthesised experience to assess the contribution of the health sector to the mortality reduction, including the roles of access, uptake and quality of services, and of health system strengthening. We explored the wider context by examining the contribution of fertility declines, and the roles of socioeconomic and human development, particularly as they affected service use, the health system and fertility. We analysed government levers, namely policies and programmes implemented, investments in data and evidence, and political commitment and financing, and we examined international inputs. We contextualised these within a mortality transition framework.We found that strategies evolved over time as the contacts women and neonates had with health services increased. The seven countries tended to align with global recommendations but could be distinguished in that they moved progressively towards implementing their goals and in scaling-up services, rather than merely adopting policies. Strategies differed by phase in the transition framework-one size did not fit all.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".