Tackling Childhood Stunting in the Eastern Mediterranean Region in the Context of COVID-19
Bibliographic record
Abstract
Over 20 million children under 5 years old in the WHO Eastern Mediterranean Region have stunted growth, as a result of chronic malnutrition, with damaging long-term consequences for individuals and societies. This review extracted and analyzed data from the UNICEF, WHO and the World Bank malnutrition estimates to present an overall picture of childhood stunting in the region. The number of children under 5 in the region who are affected by stunting has dropped from 24.5 million (40%) in 1990 to 20.6 million (24.2%) in 2019. The reduction rate since the 2012 baseline is only about two fifths of that required and much more rapid progress will be needed to reach the internationally agreed targets by 2025 and 2030. Prevalence is highest in low-income countries and those with a lower Human Development Index. The COVID-19 pandemic threatens to undermine efforts to reduce stunting, through its impact on access and affordability of safe and nutritious foods and access to important health services. Priority areas for action to tackle stunting as part of a comprehensive, multisectoral nutrition strategy are proposed. In light of the threat that COVID-19 will exacerbate the already heavy burden of malnutrition in the Eastern Mediterranean Region, implementation of such strategies is more important than ever.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".