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Abstract P180: Does Poverty Consistently Affect Food Consumption in Childhood? Evidence from a Longitudinal Study in Children

2012· article· en· W2280052276 on OpenAlexaffabout
Lisa Kakinami, Marie Lambert, Lise Gauvin, Louise Séguin, Béatrice Nikièma, Gilles Paradis

Bibliographic record

VenueCirculation · 2012
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineMcGill University
Fundersnot available
KeywordsPovertyMedicineConsumption (sociology)Logistic regressionDemographyCohortLongitudinal studyChild povertyEnvironmental healthHousehold incomeGeographyEconomics

Abstract

fetched live from OpenAlex

Background: Childhood poverty is associated with poorer food consumption patterns but longitudinal data on this association is limited. To assess if the relationship between food consumption and poverty differs depending on the child’s age and pattern of poverty, we analyzed the relationship between consumption of selected foods and poverty trajectories at various ages in a birth cohort. Methods: The 1998-2010 "Quebec Longitudinal Study of Child Development" (n=2,120) cohort was used for these analyses. Household income was measured annually with poverty defined as income below the low-income thresholds established by Statistics Canada adjusted for household size and geographic region. Frequency of children’s consumption of dairy (milk, cheese, yogurt), fruits, and vegetables were reported by parents using a food frequency questionnaire. Analyses were conducted on the 739 children with food consumption data. Trajectories of poverty at 6, 8, 10, and 12 years were characterized with latent class group analysis using maximum likelihood in a semiparametric mixture model. Multivariable logistic regression predicted the likelihood of having less than 2 servings a day of dairy, fruits and vegetables based on poverty trajectories after adjusting for age and sex. Results: The poverty trajectories were stable and fell into 1 lower exposure category (consistently low exposure (73%, n=537)) and 3 higher exposure categories (increasing: 8%, n=61; decreasing: 10%, n=73; or consistently high exposure: 9%, n=68)). Compared to children experiencing low exposure to poverty, children with increasing or high exposure to poverty were less likely to have at least two servings of fruit a day at all ages, but the results were not significant. Compared to children experiencing low exposure to poverty, children with high exposure were 55% (CI: 0.2-0.8, p=0.001), 31% (CI: 0.4-1.2, p=0.23), 67% (CI: 0.2-0.6, p<.0001), and 49% (CI: 0.3-0.8, p=0.001) less likely to have at least two servings of dairy a day at 6, 8, 10, and 12 years, respectively. Compared to children with low exposure to poverty, children with high exposure were 43% (CI: 0.3-0.9, p=0.02), 46% (CI: 0.3-0.9, p=0.02), 55% (CI: 0.3-0.8, p=0.003), and 47% (CI: 0.3-0.9, p=0.02) less likely to have at least two servings of vegetables a day at 6, 8, 10, and 12 years, respectively. Children at all ages with decreasing or increasing exposure to poverty were less likely to have at least two servings of vegetables a day, but the results were not statistically significant. Conclusion: Experiencing high exposure to poverty has consistent effects on food consumption throughout childhood. In addition, compared to children with low exposure to poverty, children with increasing or decreasing exposure were less likely to have at least 2 servings of fruits and vegetables a day, suggesting any exposure to poverty may have detrimental effects on consumption of selected foods.

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)0.004
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 score0.294
Threshold uncertainty score0.584

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.221
GPT teacher head0.442
Teacher spread0.221 · 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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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