Vitamin A Supplementation Programs and Country-Level Evidence of Vitamin A Deficiency
Why this work is in the frame
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Bibliographic record
Abstract
Vitamin A supplementation (VAS) programs targeted at children aged 6-59 months are implemented in many countries. By improving immune function, vitamin A (VA) reduces mortality associated with measles, diarrhea, and other illnesses. There is currently a debate regarding the relevance of VAS, but amidst the debate, researchers acknowledge that the majority of nationally-representative data on VA status is outdated. To address this data gap and contribute to the debate, we examined data from 82 countries implementing VAS programs, identified other VA programs, and assessed the recentness of national VA deficiency (VAD) data. We found that two-thirds of the countries explored either have no VAD data or data that were >10 years old (i.e., measured before 2006), which included twenty countries with VAS coverage ≥70%. Fifty-one VAS programs were implemented in parallel with at least one other VA intervention, and of these, 27 countries either had no VAD data or data collected in 2005 or earlier. To fill these gaps in VAD data, countries implementing VAS and other VA interventions should measure VA status in children at least every 10 years. At the same time, the coverage of VA interventions can also be measured. We identified three countries that have scaled down VAS, but given the lack of VA deficiency data, this would be a premature undertaking in most countries without appropriate status assessment. While the global debate about VAS is important, more attention should be directed towards individual countries where programmatic decisions are made.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it