Impact of Conditional Transfer Programs in Panama on Food and Nutrient Intakes and Anthropometric Status of Ngabe Preschool Children
Bibliographic record
Abstract
Conditional cash transfer (CT) and food voucher (FV) programs aim to improve child nutritional status. Our goals were: (1) to describe food group and macro‐ and micronutrient intakes of 315 Ngabe preschool children whose families participated in either a CT or a FV program and (2) to determine which foods or nutrients were associated with higher HAZ scores. We collected 3–4 24‐hr dietary recalls over 16 mo along with HAZ and measured chronicity of protozoan infection and GI nematode burden. Nutrient and food group predictors of HAZ were identified using multiple linear regression while controlling for household assets, infections and program participation. The uniformly inadequate intakes for macro‐ and micronutrients differed by program participation. Stunted children from the CT program had higher intakes of energy and carbohydrate (bread, chips/crackers, juice crystals). In contrast stunted children from the FV program had lower intakes of protein and fat. Multiple linear regression models revealed that meat, bread and juice crystals but not sweets increased HAZ in the FV region. In the CT region, pasta and corn products were positive predictors but root vegetables and green bananas were negative predictors of HAZ. However, models that included infections captured more variability (29% vs 5%) suggesting that HAZ is influenced not only by diet but also by infection. (Funding: SENACYT – Panama and IDRC – Canada)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".