Setting research priorities for maternal, newborn and child health, sexual and reproductive health and nutrition in Afghanistan: an application of the Child Health and Nutrition Research Initiative methodology
Bibliographic record
Abstract
BACKGROUND: Since 2021, Afghanistan has faced a worsening humanitarian crisis that disproportionately impacts Afghan women and children. They experience inequities in healthcare access, deterioration of healthcare quality and extreme food insecurity. This study aims to fill an important gap by providing consensus on research priorities for maternal, newborn and child health (MNCH), sexual and reproductive health (SRH) and nutrition in Afghanistan. METHODS: The Child Health and Nutrition Research Initiative (CHNRI) is a widely used research prioritisation methodology that crowdsources input from subject matter experts to generate, score and rank research questions. This study reached out to 303 Afghanistan health researchers, who were identified through relevant publications, to align on the 20 highest priority MNCH, SRH and nutrition research questions. Question generation occurred in 2022, and data collection and analysis were completed by January 2025. RESULTS: questions in MNCH and nutrition topic areas. The top questions ranged from characterising the availability, access and quality of MNCH services, to leveraging locally available interventions for malnutrition and food security, to strategies for increasing immunisation coverage. CONCLUSION: By identifying high-priority research questions, donors, researchers, implementers and governments can align their research agendas and resource allocation to address critical health challenges for women and children in Afghanistan.
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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 | 0.302 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".