The cost‐efficiency of vitamin A supplementation services in Kenya: An assessment of a Malezi Bora event in Kenya
Bibliographic record
Abstract
Vitamin A supplementation (VAS) remains a cornerstone of global child survival programs. As available funding declines, countries are seeking alternative delivery platforms. We examine a VAS-deworming delivery event in 2019 in Kenya, called Malezi Bora (MB), that employed four delivery platforms: health clinics, Early Childhood Development centers, community distribution points, and home visits. VAS coverage data were collected via household surveys in four subcounties, three of which received financial and technical assistance, and one of which received technical assistance only. Data on costs were collected using structured and semi-structured questionnaires. Only one subcounty achieved the targeted VAS coverage rate (80%) across most age subgroups; the subcounty not receiving financial assistance covered just 37% of children 6-59 months of age. Two other funded subcounties had higher coverage rates but failed to achieve 80% coverage for any age subgroup. Most children in the funded subcounties received VAS in their homes. Most children in the unfunded subcounty received VAS at a health facility. Being aware of MB was the most important factor associated with receiving VAS. Cost per child reached, including opportunity costs, varied across subcounties from $1.81 to $11.13 USD. Salaries were the main cost drivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".