Evaluating the effectiveness of a nutrition‐sensitive agriculture intervention in Western Kenya: design of the Mama SASHA cohort study of vitamin A (1019.2)
Bibliographic record
Abstract
The 2013 Lancet Maternal and Child Nutrition series identified evaluations of nutrition‐sensitive agricultural interventions as a research priority. The Mama SASHA study in Western Kenya links delivery of vitamin A (VA) rich orange‐flesh sweet potato (OFSP) to antenatal care to improve VA and nutritional status of pregnant and lactating women and their children. The evaluation strategy includes a nested longitudinal study following women and their infants from pregnancy to 9 months postpartum. VA status is assessed using infection‐adjusted plasma retinol binding protein and breastmilk retinol (postpartum). Maternal and child infection, iron, and anemia status, anthropometry, diet, health services uptake, and food security status are also measured. 505 eligible pregnant women, attending ANC at 4 control and 4 intervention facilities, were consented and enrolled. At enrollment women in control and intervention communities did not differ with respect to VA, iron, anemia or anthropometric status; food security or dietary diversity scores; demographic characteristics; awareness of vitamin A; or consumption of vitamin A rich foods in the past 7 days. Only 10 women consumed OFSP in the previous 7 days; all in intervention communities. The longitudinal study will contribute to rigorous evaluation of the OFSP intervention on maternal and child VA status and allow assessment of program impact pathways. Grant Funding Source : Bill and Melinda Gates Foundation
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".