Strategies to achieve adequate vitamin A intake for young children: options for Cameroon
Bibliographic record
Abstract
Meeting children's vitamin A (VA) needs remains a policy priority. Doing so efficiently is a fiscal imperative and protecting at-risk children during policy transitions is a moral imperative. Using the Micronutrient Intervention Modeling tool and data for Cameroon, we predict the impacts and costs of alternative VA intervention programs, identify the least-cost strategy for meeting targets nationally, and compare it to a business-as-usual (BAU) strategy over 10 years. BAU programs effectively cover ∼12.8 million (m) child-years (CY) and cost ∼$30.1 m; ∼US$2.34 per CY effectively covered. Improving the VA-fortified oil program, implementing a VA-fortified bouillon cube program, and periodic VA supplements (VAS) in the North macroregion for 3 years effectively cover ∼13.1 m CY at a cost of ∼US$9.5 m, or ∼US$0.71 per CY effectively covered. The tool then identifies a sequence of subnational policy choices leading from the BAU toward the more efficient strategy, while addressing VA-attributable mortality concerns. By year 4, fortification programs are predicted to eliminate inadequate VA intake in the South and Cities macroregions, but not the North, where VAS should continue until additional delivery platforms are implemented. This modeling approach offers a concrete example of the strategic use of data to follow the Global Alliance for VA framework and do so efficiently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".