How do we improve maternal and child health outcomes in Ghana?
Bibliographic record
Abstract
Maternal and infant mortality includes a number of health challenges in Ghana, with outcomes among the worst in the subregion and the world. Our aim here was to provide insights into how Ghana has approached these challenges, with a view to making suggestions for the future. Ghana has made significant gains in reducing infant and maternal deaths in the past decade through initiatives like the Free Maternal Care Policy, the Community-based Health Planning Services, and the National Health Insurance Policy. These policies have improved financial access to maternal and obstetric health services, facility-based delivery, and antenatal care services in particular. However, a number of challenges still hinder access to maternal and child health outcomes. Poor infrastructure, human resource challenges, poor access to essential medicines, poor quality of care, and superstitious and cultural beliefs have been noted in the literature. We suggest that while providing the necessary human and financial resources, other initiatives including the promotion of maternal health education, supervised home delivery, and zero maternal death interventions should be encouraged to help improve maternal and child health outcomes in Ghana.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".