Armed conflicts and national trends in reproductive, maternal, newborn and child health in sub-Saharan Africa: what can national health surveys tell us?
Bibliographic record
Abstract
Armed conflicts are widespread in sub-Saharan Africa and considered to be an important factor in slowing down national progress in reproductive, maternal, newborn and child health (RMNCH). The measurement of the impact of conflicts on national levels and trends in RMNCH is difficult. National surveys conducted before and sometimes during and after conflicts are a major source of information on the national and local effects of conflicts on RMNCH. We examined data from national surveys in 13 countries in sub-Saharan Africa with major conflicts during 1990-2016 to assess the levels and trends in RMNCH intervention coverage, nutritional status and mortality in children under 5 years in comparison with subregional trends. The surveys provide substantive evidence of a negative association between levels and trends in national indicators of RMNCH service coverage, child growth and under-5 mortality with armed conflict, with some notable exceptions. National surveys are an important source of data to assess the longer term national consequences of conflicts for RMNCH in most countries, despite limitations due to sampling and timing of the surveys.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".