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). Addressing factors affecting the continuum of care from antenatal (ANC), delivery and postnatal care (PNC) remains critical (Yasuoka et al. , -2018). Low MNCH services utilization can be attributed to two types of factors: user factors and health system factors. User factors include distance to health facility, cultural beliefs, practices and economic factors. Health system factors include competence and attitude of healthcare providers, infrastructure and equipment and organization of the health system (Kisiangani et al. , -2020). MNCH is often disproportionately affected by conflicts, natural disasters and pandemics. On 11 March 2020, COVID-19 was declared a global pandemic by the World Health Organization (WHO) (WHO, -2020), with devastating health and social consequences (Hausmann, -2020).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".