Financial costs of Mama‐SASHA ő a project to improve health and nutrition through an integrated orange flesh sweet potato production and health service delivery model (132.6)
Bibliographic record
Abstract
Mama‐SASHA project aims to improve the health status of pregnant women and nutritional status of children up to two years through an integrated orange‐flesh sweet potato (OFSP) and health service delivery strategy in Western Kenya. Nutrition education and vouchers for OFSP vines are provided during antenatal care visits, with additional nutrition education and support provided in the communities and through pregnant mothers clubs (PMCs). The purpose of this study is to analyze the financial costs of the Mama‐SASHA project. We use a microcosting approach based on project expense reports to estimate financial costs during the period 2011‐2013, allocating costs by activity and inputs by implementing organization. Project monitoring data were used to estimate project output and number of beneficiaries reached. Financial costs were incurred by two agricultural NGOs and one health NGO, totaling $344,860 (USD). Over 5,400 women participated in monthly PMCs at a cost of $63 per woman. Of 4,629 women who received vouchers, 3,281 women redeemed vouchers and planted OFSP at a cost of $105 per beneficiary. There are limited comparable cost estimates in the literature; however, this estimate falls in the range of published cost estimates for community‐based therapeutic feeding programs supported by community health workers with some referrals to health centers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".