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Record W6905019480 · doi:10.14288/1.0379517

Breakfast in Canada: Prevalence of Consumption, Contribution to Nutrient and Food Group Intakes, and Variability across Tertiles of Daily Diet Quality. A Study from the International Breakfast Research Initiative

2019· article· en· W6905019480 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2019
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsnot available
Fundersnot available
KeywordsNutrientAdded sugarFood groupVitaminBreakfast cerealMicronutrientBody mass indexObesityFood intake

Abstract

fetched live from OpenAlex

This study used 24-h recall data from the nationally representative 2015 Canadian Community Health Survey-Nutrition to assess breakfast intake among Canadians aged 6–12 years (n = 2331), 13–17 years (n = 2026), 18–54 years (n = 7651), and 55+ years (n = 6279). Overall, 90% consumed breakfast; breakfast consumers reported higher intakes of energy and key nutrients and had higher daily diet quality scores assessed using the Nutrient-Rich Foods Index 9.3 (NRF 9.3). Among breakfast consumers (n = 16,484), breakfast contributed a mean of 389 kcal (1628 kJ) and 21.6% of daily energy intake. Relative to its contribution to energy, breakfast contributed higher intakes of fruit, whole grains, and fluid milk, as well as associated nutrients (e.g., carbohydrate, total sugars, fiber, calcium, and vitamin D). Among breakfast consumers classified by daily dietary quality (NRF 9.3 score), energy intake at breakfast did not differ across tertiles for either children or adults. However, intakes of key nutrients, fiber, and total sugars increased across tertiles, and among adults, intakes of saturated fat and sodium decreased. Mean intakes of fruit, whole grains, and fluid milk also increased across tertiles, as did the proportion of individuals consuming these foods; higher fruit and milk intakes may explain higher sugar intakes as diet quality increased. Promoting the consumption of these foods at breakfast could contribute to improved diet quality among Canadians.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.048
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.385
Teacher spread0.319 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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