MétaCan
Menu
Back to cohort

Cost estimations of different types of micronutrient supplements for children and pregnant women

2008· article· en· W172387125 on OpenAlexaff
Arantxa Colchero, Lynnette M. Neufeld

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsMicronutrientEnvironmental healthMedicinePublic healthDeveloping countryCost effectivenessCost–benefit analysisCost databasePediatricsDemographyEconomic growthEconomicsPolitical scienceNursing

Abstract

fetched live from OpenAlex

Few studies have attempted to compare the cost of different types of micronutrient supplements that are commonly used in public health programs. The objective of this study was to assess the costs of three types of nutritional supplements with identical micronutrient content: fortified food (FF) currently provided to children and pregnant and lactating women in Mexico's Oportunidades program; syrup (SY) for children or tablets for women and; Sprinkles for children and women (SK). We estimated the cost per recommended dose of acquiring (with delivery in Mexico City) and distributing the supplements, assuming distribution by the public sector. Indirect costs to beneficiaries or families were not assessed. Sensitivity analysis was conducted on uncertain parameters. The average cost per dose of FF for children was US$0.13 and US$0.19 for women. The average cost for SY was US$0.17 and the tablets US$0.07 and average cost per dose of SK was US$0.03 (for children or women). The information obtained in this analysis is a necessary complement to impact and acceptability data to assist decision makers regarding the most appropriate and feasible micronutrient supplement for public programs. The Oportunidades program funded this research and holds the rites to the data obtained.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.025
GPT teacher head0.277
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicChild Nutrition and Water AccessFrench-language works237,207