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Record W3016027866 · doi:10.1016/j.jand.2020.01.011

Lunch on School Days in Canada: Examining Contributions to Nutrient and Food Group Intake and Differences across Eating Locations

2020· article· en· W3016027866 on OpenAlexfundaboutno aff
Claire N. Tugault-Lafleur, Jennifer Black

Bibliographic record

VenueJournal of the Academy of Nutrition and Dietetics · 2020
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsNutrientEnvironmental healthFood groupMealMedicineCalorieDemographyDescriptive statisticsGerontologyMathematicsBiologyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Recent federal proposals in Canada have called for changes in the delivery and funding of school lunches. Yet little evidence has documented the nutritional quality of meals eaten by school children, which is needed to inform school lunch reforms. OBJECTIVES: To assess the dietary contributions of lunch foods to daily food and nutrient intakes on school days and compare dietary intakes across eating locations (school, home, and off campus). DESIGN: Cross-sectional analyses of school day data from the 2015 Canadian Community Health Survey-Nutrition. PARTICIPANTS/SETTING: Children aged 6 to 17 years who completed a 24-hour dietary recall falling on a school day in 2015 (n=2,540). MAIN OUTCOME MEASURES: Mean percent of daily intakes of energy, food groups, and nutrients contributed by foods reported at lunch and energy-adjusted intakes of nutrients and food groups consumed during the lunch meal. STATISTICAL ANALYSES: Descriptive statistics were used to assess the percent of daily energy, nutrients, and food groups contributed by lunch foods. Multivariable linear regression models examined differences in dietary outcomes across eating locations for the full sample and stratified by age group, with separate models for children aged 6 to 13 and 14 to 17 years. RESULTS: On average, foods reported at lunch provided ∼26% of daily calories on school days. Relative to energy, lunch foods provided lower contributions of dark green and orange vegetables, whole fruit, fruit juice, whole grains, milk and alternatives, fluid milk; minimally nutritious foods including sugar-sweetened beverages; and several related nutrients including total sugars; vitamins A, D, B-6, and B-12; riboflavin; and calcium. Yet, lunch foods provided proportionally higher contributions of grain products, non-whole grains, meat and alternatives, and sodium. Children aged 14 to 17 years who ate lunch at school reported higher intakes of total vegetables and fruit, whole fruit, whole grains, fiber, vitamin C, and magnesium but reported fewer calories from sugar-sweetened beverages compared with their peers who ate lunch off campus. CONCLUSIONS: Relative to its contribution to energy, lunch on school days contributed to proportionally lower intakes of many healthful foods such as dark green and orange vegetables, whole fruit, whole grains, and fluid milk but also proportionally lower intakes of other high-fat and high-sugar foods including sugar-sweetened beverages. This study adds to the growing body of evidence on dietary concerns during school time for Canadian children and highlights particular nutritional challenges for adolescents consuming lunch off campus.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.031
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.006
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.303
Teacher spread0.258 · 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

Citations26
Published2020
Admission routes2
Has abstractno

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