The relationship between armed conflict and reproductive, maternal, newborn and child health and nutrition status and services in northeastern Nigeria: a mixed-methods case study
Bibliographic record
Abstract
BACKGROUND: Armed conflict between the militant Islamist group Boko Haram, other insurgents, and the Nigerian military has principally affected three states of northeastern Nigeria (Borno, Adamawa, Yobe) since 2002. An intensification of the conflict in 2009 brought the situation to increased international visibility. However, full-scale humanitarian intervention did not occur until 2016. Even prior to this period of armed conflict, reproductive, maternal, neonatal, and child health indicators were extremely low in the region. The presence of local and international humanitarian actors, in the form of United Nations agencies and non-governmental organizations, working in concert with concerned federal, state, and local entities of the Government of Nigeria, were able to prioritize and devise strategies for the delivery of health services that resulted in marked improvement of health status in the subset of the population in which this could be measured. Prospects for the future remain uncertain. METHODS: Interviews were conducted with more than 60 respondents from government, United Nations agencies, and national and international non-governmental organizations. Quantitative data on intervention coverage indicators from publicly available national surveys (Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS)), National Nutrition and Health Surveys (NNHS)) were descriptively analyzed. RESULTS: Overall, indicators of low reproductive, maternal, neonatal, and child health (RMNCH) status and intervention coverage were found in the pre-intervention period (prior to 2016) and important improvements were noted following the arrival of international humanitarian assistance, even while armed conflict and adverse conditions persisted. Security issues, workforce limitations, and inadequate financing were frequently cited obstacles. CONCLUSION: It is assumed that armed conflict would have a negative impact on the health status of the affected population, but pre-conflict indicators can be so depressed that this effect is difficult to measure. When this is the case, health sector intervention by the international community can often result in marked improvements in the accessible population. What might happen upon the departure of the humanitarian organizations cannot be predicted with an appreciable degree of certainty.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".