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Contextual Influences on Child Feeding in Two South Asian Immigrant Groups

2020· article· en· W3033990811 on OpenAlexvenueno aff
Alison Karasz, Margia Shiriti, Nilifa Desilva, Afrida Khurshid, Karen Bonuck, Usha Ramachandran

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health Disparities
KeywordsMedicineImmigrationDemography

Abstract

fetched live from OpenAlex

Child obesity is a major health disparity, with low-income communities bearing a disproportionate burden of disease. Although the powerful influence of socio-economic status on child diet and feeding practices is well established, its local contextual mechanisms are not necessarily well understood. This study used a qualitative comparative design to explore the socio-cultural context of child feeding ethnically similar but economically diverse immigrant families. Seventeen in-depth qualitative interviews conducted with affluent and low-income immigrant mothers from the South Asian subcontinent. We sought to explore potential contextual influences on maternal feeding practices, including: maternal beliefs and values, patterns of help and information seeking, family and household, and impacts of the chrono-system—the role of memory and life transitions. Our findings help to explain mechanisms underlying SES disparities in child obesity in this group, and maybe helpful in designing tailored interventions aimed at reducing these disparities.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.429
Teacher spread0.347 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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