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Record W2912765309 · doi:10.1093/pubmed/fdy201

Stunting, dietary diversity and household food insecurity among children under 5 years in ethnic communities of northern Thailand

2018· article· en· W2912765309 on OpenAlexfundno aff
Anna Roesler, Lisa G. Smithers, Prasit Wangpakapattanawong, Vivienne Moore

Bibliographic record

VenueJournal of Public Health · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsDietary diversityBreastfeedingEthnic groupEnvironmental healthFood insecurityFood securityDiversity (politics)Stunted growthCross-sectional studyMedicineGeographyDemographyMalnutritionPediatricsAgriculture

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to describe stunting in infants and young children in the ethnic communities of northern Thailand and to explore associations with dietary diversity and household factors including food security. METHODS: A cross-sectional survey of households with children under 5 years from eight villages. Adult respondents provided information on foods consumed by each child and details of the household. Heights and weights of children were measured. RESULTS: Adults from 172 households and 208 children participated. Overall, 38% of children were stunted. Exclusive breastfeeding was rare, but the proportion consuming breastmilk at 24 months (75%) was high. Few children (7%) aged 6-11 months met minimum dietary diversity. Stunted children were less likely than non-stunted children to meet minimum dietary diversity (63 versus 82%). Widespread food insecurity did not discriminate between stunted and non-stunted children. Stunting was elevated when households had little land and few animals. CONCLUSIONS: Stunting was widespread in children under 5 years of age, in part reflecting poor dietary diversity, especially at age 6-11 months. Stunting was worst in households with least assets. Small increases in land or animals, or equivalent resources, appear to be required to improve child nutrition in extremely poor families.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.132
GPT teacher head0.308
Teacher spread0.176 · 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 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

Citations37
Published2018
Admission routes1
Has abstractyes

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