Delivering nutrition interventions to women and children in conflict settings: a systematic review
Bibliographic record
Abstract
BACKGROUND: Low/middle-income countries (LMICs) face triple burden of malnutrition associated with infectious diseases, and non-communicable diseases. This review aims to synthesise the available data on the delivery, coverage, and effectiveness of the nutrition programmes for conflict affected women and children living in LMICs. METHODS: We searched MEDLINE, Embase, CINAHL, and PsycINFO databases and grey literature using terms related to conflict, population, and nutrition. We searched studies on women and children receiving nutrition-specific interventions during or within five years of a conflict in LMICs. We extracted information on population, intervention, and delivery characteristics, as well as delivery barriers and facilitators. Data on intervention coverage and effectiveness were tabulated, but no meta-analysis was conducted. RESULTS: Ninety-one pubblications met our inclusion criteria. Nearly half of the publications (n=43) included population of sub-Saharan Africa (n=31) followed by Middle East and North African region. Most publications (n=58) reported on interventions targeting children under 5 years of age, and pregnant and lactating women (n=27). General food distribution (n=34), micronutrient supplementation (n=27) and nutrition assessment (n=26) were the most frequently reported interventions, with most reporting on intervention delivery to refugee populations in camp settings (n=63) and using community-based approaches. Only eight studies reported on coverage and effectiveness of intervention. Key delivery facilitators included community advocacy and social mobilisation, effective monitoring and the integration of nutrition, and other sectoral interventions and services, and barriers included insufficient resources, nutritional commodity shortages, security concerns, poor reporting, limited cooperation, and difficulty accessing and following-up of beneficiaries. DISCUSSION: Despite the focus on nutrition in conflict settings, our review highlights important information gaps. Moreover, there is very little information on coverage or effectiveness of nutrition interventions; more rigorous evaluation of effectiveness and delivery approaches is needed, including outside of camps and for preventive as well as curative nutrition interventions. PROSPERO REGISTRATION NUMBER: CRD42019125221.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".