An integrated agriculture‐nutrition‐health program increased maternal knowledge on health and nutrition in Western Kenya
Bibliographic record
Abstract
The Mama SASHA project integrated an orange flesh sweet potato (OFSP)‐focused, agricultural‐nutrition education intervention into delivery of routine health services and pregnant women's clubs. The aim was to improve the diets, nutrition and health of pregnant / lactating women and children. We enrolled 505 women from intervention and control facilities at their first ANC visit in early pregnancy and followed them to 9 months postpartum. The proportion of intervention women who had heard of VA increased significantly from 43% to 75% (p<0.05); no change was observed among controls (24.3% at enrollment and 25.9% at 9 mos). Only 2.6% of enrolled mothers could identify any functions of VA at enrollment; 5% could name 3 VA rich foods. By 9 months postpartum, 10.9% of intervention mothers vs. 4.2% of control mothers, could identify 2 functions of VA (p<0.05); 15% of intervention and 12% of control mothers could identify 3 VA rich foods. Awareness of early initiation, exclusive breastfeeding to 6 months, continued breastfeeding to 2 years and optimal complementary feeding practices increased in both groups; however the increase in the proportion indicating that mothers should initiate breastfeeding within 1 hour of birth was greater among intervention mothers. There were no differences in knowledge of other infant feeding practices. An integrated agriculture‐nutrition‐health linkages project increased VA knowledge but had limited benefit beyond standard counseling on awareness of other optimal infant and young child feeding practices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".