A scoping review of research on policies to address child undernutrition in the Millennium Development Goals era
Bibliographic record
Abstract
OBJECTIVE: The breadth of research on the impact of nutrition-specific policies to address child undernutrition is not well documented. This review maps the evidence base and identifies gaps on such policies. DESIGN: We systematically searched Medline, Embase, PAIS Index for public policy, Scopus and Web of Science databases to identify eligible studies. Key study characteristics, including research design, type of policy, time span of policy before impact assessment, child age at outcome assessment and types of outcomes assessed, were abstracted in duplicate. SETTING: Low-, middle- and high-income countries. PARTICIPANTS: Studies were eligible for inclusion if they aimed to assess the impact of population-level nutrition-specific policies on undernutrition among children under 10 years of age. RESULTS: Of the 5646 abstracts screened, eighty-three studies were included. A range of policies to address child undernutrition were evaluated; the majority were related to micronutrient fortification. Most studies were observational, reported on mandatory regional or sub-national polices, were conducted in high-income countries and evaluated policies within 1 year of implementation. A narrow set of health outcomes were evaluated, most commonly iodine deficiency disorders and neural tube defects. CONCLUSIONS: Nutrition policies were commonly associated with improved child nutritional status and health. However, this evidence is primarily based on limited settings and on a limited number of outcomes. Further research is needed to assess the longer-term impact of a broader range of nutrition policies on child health, particularly in low- and middle-income countries.
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 | 0.032 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.025 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".