Tackling Protein-Calorie Malnutrition during World Crises
Bibliographic record
Abstract
Undernutrition is still highly prevalent in developing countries and leads to a multitude of problems as it weakens the immune system, which leads to increased risk of infections and diet-related diseases. COVID-19 has worsened the existing situation and has resulted in unprecedented health, social, and economic disruptions across the world. Before COVID-19, about 54% children under 5 years were moderately or seriously malnourished, and after the COVID-19 pandemic, early estimates suggest that an additional 2.6 million children were stunted; 9.3 million were wasted, with an addition of 2.1 million maternal anemia cases; 168,000 child deaths; and USD 29.7 billion in productivity losses. This review is mainly focused on the health and nutrition sectors and highlights the impact of COVID-19 on malnutrition, food system and industry, and it also discusses the various measures implemented across the world to cater the burden of maternal and child malnutrition. Movement restrictions and lockdowns within and across the countries/borders have imposed an unprecedented stress and shock on the food supply chain, affecting harvest, food processing, supply, logistics, food demand, shortages, and cost. Many countries have implemented interventions such as cash transfers, food ration distribution, insurance plans, utility subsidy, and tax exemptions to assist the population to cope with the financial and health issues caused due to the outbreak. Other than these measures, evidence recommends some essential direct and indirect interventions which could help in reducing malnutrition during COVID-19. The COVID-19 pandemic has re-demonstrated the connection between food systems, nutrition, health, and prosperity and the need for a more holistic approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".