Tackling malnutrition in Latin America and the Caribbean: challenges and opportunities.
Bibliographic record
Abstract
Undernutrition and micronutrient deficiencies are still a public health problem in Latin America and the Caribbean (LAC), and overweight and obesity have reached epidemic proportions. To assess the nutrition landscape in LAC countries and guide future nutrition efforts and investments, the Pan American Health Organization and the Micronutrient Initiative joined efforts to 1) identify information gaps and describe the current nutritional situation in the region; 2) map existing policies to address malnutrition in Latin America; 3) describe the impact of conditional cash transfer programs (CCTs) on nutrition and health outcomes; and 4) identify the challenges and opportunities to address malnutrition in the region. This article summarizes the methods and key findings from that research and describes the current challenges and opportunities in addressing malnutrition in the LAC region. LAC countries have advanced in reducing undernutrition and micronutrient deficiencies, but important gaps in information are a major concern. These countries have policies to address undernutrition and micronutrient deficiencies, but comprehensive and intersectoral policies to tackle obesity are lacking. CCTs in Brazil, Colombia, and Mexico have been reported to have a positive impact on child nutrition and health outcomes, providing an opportunity to integrate nutrition actions in intersectoral platforms. The current epidemiological situation and policy options offer an opportunity for countries, technical agencies, donors, and other stakeholders to jointly scale up nutrition actions. This can support the development of comprehensive and intersectoral policies to tackle the double burden of malnutrition, strengthen national nutrition surveillance systems, incorporate monitoring and evaluation as systematic components of policies and programs, document and increase investments in nutrition, and assess the effectiveness of such policies to support political commitment and guarantee sustainability.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".