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Record W3116856123 · doi:10.1080/19439342.2020.1828998

Mothers’ education and the effectiveness of nutrition programmes: evidence from a matched cross-sectional study in rural Bangladesh

2020· article· en· W3116856123 on OpenAlexafffund
Thomas de Hoop, Shelby Fallon, Fakir Md Yunus, Sabeth Munrat, Saira Parveen Jolly, Farzana Sehrin, Bachera Aktar, Ruhina Binta A Ghani, Joshua Sennett

Bibliographic record

VenueJournal of Development Effectiveness · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Saskatchewan
FundersEuropean Social FundNational Institute for Occupational Safety and HealthNational Institute of Child Health and Human DevelopmentNorwegian Institute of Public HealthNIHR Oxford Biomedical Research CentreJapan Society for the Promotion of ScienceNational Health and Medical Research CouncilQatar National Research FundUniversity of North Carolina at Chapel HillGillings School of Public HealthCollege of Pharmacy, University of MichiganNational Institutes of HealthMedical Research CouncilAfrican Academy of SciencesSecretaría Nacional de Ciencia, Tecnología e InnovaciónNational Research University Higher School of EconomicsDeakin UniversityUniversidad de Costa RicaUniversiti Kebangsaan MalaysiaVetenskapsrådetKuwait UniversityAcademy of FinlandMax-Planck-GesellschaftUniversity College DublinPublic Health AgencyConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of GhanaQueensland GovernmentCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCarolina Population Center, University of North Carolina at Chapel HillFonds National de la Recherche LuxembourgAutoritatea Natională pentru Cercetare StiintificăInstituto de Salud Carlos IIINational Natural Science Foundation of ChinaDepartment of Biotechnology, Ministry of Science and Technology, IndiaComunidad de MadridNational Heart Foundation of AustraliaThe Wellcome Trust DBT India AlliancePeking UniversityNational Research FoundationNational Institute for Health and Care ResearchEuropean CommissionDepartment of Science and Innovation, South AfricaNational Authority for Scientific Research and InnovationPublic Health EnglandXiamen UniversityMinisterio de Ciencia, Innovación y UniversidadesWellcome TrustMinistério da Ciência, Tecnologia e Ensino SuperiorUniversity of CalgaryBundesministerium für Bildung und ForschungBritish Heart FoundationScottish GovernmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBHF Centre of Research Excellence, OxfordNational Institute of Mental HealthDanmarks GrundforskningsfondNational Aeronautics and Space AdministrationNational Institute on AgingAlexander von Humboldt-StiftungFundação para a Ciência e a TecnologiaBill and Melinda Gates FoundationYale UniversityUnited Nations Population FundUnited States Agency for International DevelopmentPublic Health Agency of CanadaBloomberg PhilanthropiesHealth Research Council of New ZealandUniversity of New South WalesMinistero della SaluteQueensland HealthChina Medical UniversityUniversity of Michigan
KeywordsBeneficiaryWastingDietary diversityEnvironmental healthMedicineCross-sectional studyNutrition EducationSocioeconomicsGerontologyGeographyAgriculturePolitical scienceEconomicsFood security

Abstract

fetched live from OpenAlex

BRAC Bangladesh trains community health workers to communicate about nutrition in its Maternal, Newborn and Child Health programme. We estimate the programme’s impact on nutrition outcomes among rural Bangladeshi children of two years and younger. We find positive effects on dietary diversity, and show that the programme reduces stunting with 7 percentage points using data from 1600 households in 40 beneficiary mouzas and 40 comparison mouzas. We find larger effects for households where primary caregivers have finished primary school. We did not find effects on wasting, which in contrast to stunting is higher among children with primary caregivers without education.

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)0.003
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.313
Teacher spread0.292 · 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
Published2020
Admission routes2
Has abstractyes

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