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Record W4285034738 · doi:10.3390/children9071028

Intake of Fruits, Vegetables, and Sugar-Sweetened Beverages among a Sample of Children in Rural Northern Ontario, Canada

2022· article· en· W4285034738 on OpenAlexafffundabout
Brenton Button, Louise W. McEachern, Gina Martin, Jason Gilliland

Bibliographic record

VenueChildren · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsLawson Health Research InstituteAthabasca UniversityChildren’s Health Research InstituteUniversity of WinnipegWestern University
FundersChildren’s Health FoundationCanadian Institutes of Health ResearchSocial Sciences and Humanities Research CouncilHeart and Stroke Foundation of Canada
KeywordsEnvironmental healthOddsResidenceIndigenousSugarMedicineRural areaOdds ratioLogistic regressionDemographyGeographyFood scienceBiology

Abstract

fetched live from OpenAlex

There is evidence to suggest that dietary intake of children differs by rural/urban place of residence: rural children may have a higher intake of foods high in fat and sugar than those living in urban environments. The aim of this study was to examine the intake of fruits and vegetables (FV) and the frequency of sugar-sweetened beverage (SSB) consumption, among a sample of rural children in Northern Ontario, Canada, in two different seasons. Sociodemographic factors and children's FV and SSB intake were measured using two repeated cross-sectional surveys, and seasonal information was based on the month of data collection. Logistic regressions were used to examine the odds of children eating five or more FVs, and the odds of 'frequently or always' consuming SSBs. During the fall, children reported eating five or more FV more often, when compared to winter (53.9% vs. 48.3%). In the fall, 25.8% of children reported 'frequently or always' drinking SSB, compared with 16.9% in winter. Indigenous children were less likely to eat five or more FV (OR 0.34 (95% CI 0.12-0.95)) in the fall when compared to non-Indigenous children. Findings indicate that intake of FV among rural students in this region is low, and the frequency of SSB is high, when compared with national recommendations.

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)0.000
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 score0.008
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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