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Record W2891630900 · doi:10.3386/w25059

Early Stimulation and Nutrition: The Impacts of a Scalable Intervention

2018· report· en· W2891630900 on OpenAlexfundno aff
Orazio Attanasio, Helen Baker‐Henningham, Raquel Bernal, Costas Meghir, Diana Pineda, Marta Rubio‐Codina

Bibliographic record

VenueNational Bureau of Economic Research · 2018
Typereport
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersNational Institutes of HealthInstituto Colombiano de Bienestar FamiliarGrand Challenges CanadaUniversity College LondonYale University
KeywordsStimulationIntervention (counseling)ScalabilityBusinessEconomicsNatural resource economicsComputer scienceMedicineNeurosciencePsychologyDatabase

Abstract

fetched live from OpenAlex

Early Childhood Development is becoming the focus of policy worldwide. However, the evidence on the effectiveness of scalable models is scant, particularly when it comes to infants in developing countries. In this paper we describe and evaluate with a cluster-RCT an intervention designed to improve the quality of child stimulation within the context of an existing parenting program in Colombia, known as FAMI. The intervention improved children's development by 0.16 of a standard deviation (SD) and children's nutritional status, as reflected in a reduction of 5.8 percentage points of children whose height-for-age is below -1 SD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.225
GPT teacher head0.501
Teacher spread0.275 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations38
Published2018
Admission routes1
Has abstractyes

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