Designing, implementing and assessing effectiveness of integrating agriculture and health to improve nutrition outcomes: the evaluative process for the Mama SASHA project (132.5)
Bibliographic record
Abstract
Responding to the call for improved evaluation of agriculture’s impact on nutrition, we describe the evaluation process that shaped the Mama SASHA project from its inception and highlight critical findings at each stage. We started with formative research to clarify how to introduce vitamin A rich orange‐fleshed sweet potato (OFSP) to pregnant women through antenatal health visits and supportive community support, deciding on vouchers for vines. We then developed an impact pathway that informed project monitoring indicators, a first round of operational research (OR) to refine the design of the project, a second round of OR to assess feasibility and acceptability and a household survey and complementary cohort study that will evaluate impact on vitamin A status and health outcomes of pregnant women and their children. In this community, where vitamin A deficiency affects approximately 20% of pregnant women and children under 2 years, the integrated agriculture and health approach was feasible and acceptable among implementers, stakeholders and beneficiaries, reaching 3,281 pregnant or lactating women with vouchers and OFSP vines.
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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 | 0.332 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".