Using ICT to research maternal, newborn and child health during the COVID-19 pandemic in Nigeria
Bibliographic record
Abstract
Research remains vital to realizing the Sustainable Development Goals (SDGs) (Fayomi et al., -2018), however it may be hampered by unexpected crises. Reducing global maternal mortality to less than 70 per 100,000 live births, neonatal mortality to 12 per 1,000 live births and under-five mortality rates to 25 per 1,000 live births are key SDG targets for 2030 (WHO, -2018). Improving access to quality maternal, newborn and child health (MNCH) services is central to achieving these targets. Despite progress in reducing maternal and neonatal mortality, Nigeria still contributes significantly to the global burden of maternal deaths, accounting for the highest proportion of stillbirths, pregnancy-related deaths and neonatal mortalities worldwide. In 2017, Nigeria accounted for almost a quarter of maternal deaths globally – the highest of any country that year (World Bank, -2019).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".