The Multiple Burden of Malnutrition in Middle-Income Countries: Challenges and Policies
Bibliographic record
Abstract
The phenomenon of the multiple burden of malnutrition, which includes undernutrition, overweight, obesity, and micronutrient deficiencies, presents a significant public health challenge in middle-income countries (MICs). However, there is scanty literature that examines the MICs as a separate group, with most combining the MICS with the low-income countries. Most of the studies explore the malnutrition issue in the LMICs (Low and middle-income countries). Given their importance in the global landscape, the MICs deserve a separate examination. This study presents the status, trends and burden of malnutrition in the MICs and uses color mapping schemes to identify malnutrition hotspots. The analysis points to the existence of multiple burdens of malnutrition in these countries. The study reviews the drivers of malnutrition. The results suggest that effective strategies must integrate multi-sectoral approaches that address the shared drivers of malnutrition, including food insecurity and dietary patterns.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".