The nutrition transition and the double burden of malnutrition
Bibliographic record
Abstract
Chronic noncommunicable diseases are increasingly frequent in low- and medium-income countries, but problems of malnutrition, such as growth restriction in children or micronutrient deficiencies in both children and adults, persist in these same countries. This double burden of malnutrition and the emergence of chronic diseases such as type 2 diabetes strain healthcare systems and constitute a sometimes unbearable load for the countries concerned, for the government, but also for the individuals affected and their families. This double burden is often associated with the nutrition transition or the progression away from the local traditional diet towards a Westernized diet frequently high in fat, salt, and sugar, with low nutritional density. This transition is attributed to worldwide changes in dietary systems expressed by an increased availability of foodstuffs marketed across the planet, such as vegetable oils, sugars, and refined flours, but also the multiplication of points of sale of food that has been processed, even ultraprocessed. The efforts to battle this scourge must take into account the complexity of the phenomenon and the many factors associated with it. A systemic approach that considers the global forces governing the food systems must be promoted. Actions concerning nutrition must therefore emphasize simultaneously the problems of undernutrition and of overnutrition. WHO labels these interventions "double duty actions."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".