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Record W2942139930

The Influence of a Centrally-Procured School Food Program on Consumption and Instances of Fruits and Vegetables in School-Age Children

2019· article· en· W2942139930 on OpenAlexaboutno aff
Kimberly D. Charbonneau

Bibliographic record

VenueScholarship@Western (Western University) · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Food consumptionEnvironmental healthGeographyAgricultural economicsMedicineSociologyEconomicsSocial science
DOInot available

Abstract

fetched live from OpenAlex

Background: In Canada, 70% of youth are not meeting the recommended five servings of fruits and vegetables (FV) daily. School nutrition programs are one strategy for improving dietary habits in youth.\nMethods: A two-year pilot cluster randomized controlled trial was implemented within Southwestern Ontario to assess how a ten-week centrally-procured school food program (CPSFP) influences students’ consumption and instances of FV compared to the traditional school nutrition program (TSNP).\nResults: Children were 9-13 years of age; 30 schools received the CPSFP and 30 received the TSNP. Vegetable consumption did not change with the CPSFP (mean=0.0; SD=1.0) or the TSNP (mean=0.0; SD=1.0; p=0.94). Fruit consumption did not change with the CPSFP (mean=0.0; SD=1.4) and decreased by 0.1 servings (SD=1.4) with the TSNP (p=0.06). Instances of vegetables and fruit were similar between groups.\nConclusions: The CPSFP resulted in no significant change in consumption or instances of FV.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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