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Comparison of Maternal Feeding Practices and Child Weight Status in Children from Three Countries

2014· article· en· W2097900808 on OpenAlexvenueno aff
Keith E. Williams, Maria Arlete M. Schimith-Escrivão, Kyong‐Mee Chung, Woo Hyun Jung, Helen M. Hendy

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersChildren's Miracle Network HospitalsPennsylvania State UniversityUniversity of Pennsylvania
KeywordsMedicineEnvironmental healthPediatricsDemography

Abstract

fetched live from OpenAlex

The present study considered three samples of mothers from Brazil, South Korea, and the United States to determine whether mothers demonstrate a consistent pattern of feeding practices associated with child overweight. Participants included 1204 mothers of children 6-10 years old. Mothers completed questionnaires to report their children's demographics and their feeding practices with the Parent Mealtime Action Scale (PMAS). The South Korean children showed significantly less obesity (10.4%) than children from Brazil (17.0%) or the United States (19.6%). Confirmatory factor analysis for mothers from all three samples revealed good fit for the same nine PMAS dimensions of feeding practice. Hierarchical multiple regression revealed that after taking into account child age and gender, heavier child weight was found associated with more Fat Reduction and less Insistence on Eating by mothers from all three samples. Results from past experimental research suggest that these two maternal feeding practices would be counter-productive for teaching children's self-regulation of diet and weight management. Alternative maternal feeding practices are suggested.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.341
Teacher spread0.324 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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