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Record W2626926510 · doi:10.1186/s12889-017-4393-6

Food insecurity and food consumption by season in households with children in an Arctic city: a cross-sectional study

2017· article· en· W2626926510 on OpenAlexafffundabout
Catherine Huët, James D. Ford, Victoria L. Edge, Jamal Shirley, Nia King, Sherilee L. Harper

Bibliographic record

VenueBMC Public Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of GuelphPublic Health Agency of CanadaNunavut Research InstituteNunavut Arctic CollegeMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNasivvik Centre for Inuit Health and Changing EnvironmentsArcticNetInternational Development Research CentreMcGill University
KeywordsFood securityEnvironmental healthFood insecuritySocioeconomic statusBiostatisticsMedicineConsumption (sociology)Cross-sectional studySocioeconomicsDemographyEpidemiologyAgricultureGeographyPopulationEconomics

Abstract

fetched live from OpenAlex

High rates of food insecurity are documented among Inuit households in Canada; however, data on food insecurity prevalence and seasonality for Inuit households with children are lacking, especially in city centres. This project: (1) compared food consumption patterns for households with and without children, (2) compared the prevalence of food insecurity for households with and without children, (3) compared food consumption patterns and food insecurity prevalence between seasons, and (4) identified factors associated with food insecurity in households with children in Iqaluit, Nunavut, Canada. Randomly selected households were surveyed in Iqaluit in September 2012 and May 2013. Household food security status was determined using an adapted United States Department of Agriculture Household Food Security Survey Module. Univariable logistic regressions were used to examine unconditional associations between food security status and demographics, socioeconomics, frequency of food consumption, and method of food preparation in households with children by season. Households with children ( n = 431) and without children ( n = 468) participated in the survey. Food insecurity was identified in 32.9% (95% CI: 28.5–37.4%) of households with children; this was significantly higher than in households without children (23.2%, 95% CI: 19.4–27.1%). The prevalence of household food insecurity did not significantly differ by season. Demographic and socioeconomic characteristics of the person responsible for food preparation, including low formal education attainment (OR Sept = 4.3, 95% CI: 2.3–8.0; OR May = 3.2, 95% CI: 1.8–5.8), unemployment (OR Sept = 1.1, 95% CI: 1.1–1.3; OR May = 1.3, 95% CI: 1.1–1.5), and Inuit identity (OR Sept = 8.9, 95% CI: 3.4–23.5; OR May = 21.8, 95% CI: 6.6–72.4), were associated with increased odds of food insecurity in households with children. Fruit and vegetable consumption (OR Sept = 0.4, 95% CI: 0.2–0.8; OR May = 0.5, 95% CI: 0.2–0.9), as well as eating cooked (OR Sept = 0.5, 95% CI: 0.3–1.0; OR May = 0.5, 95% CI: 0.3–0.9) and raw (OR Sept = 1.7, 95% CI: 0.9–3.0; OR May = 1.8, 95% CI: 1.0–3.1) fish were associated with decreased odds of food insecurity among households with children, while eating frozen meat and/or fish (OR Sept = 2.6, 95% CI: 1.4–5.0; OR May = 2.0, 95% CI: 1.1–3.7) was associated with increased odds of food insecurity. Food insecurity is high among households with children in Iqaluit. Despite the partial subsistence livelihoods of many Inuit in the city, we found no seasonal differences in food security and food consumption for households with children. Interventions aiming to decrease food insecurity in these households should consider food consumption habits, and the reported demographic and socioeconomic determinants of food insecurity.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.142
GPT teacher head0.419
Teacher spread0.278 · 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.

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

Citations45
Published2017
Admission routes3
Has abstractyes

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