MétaCan
Menu
Back to cohort
Record W4323037212 · doi:10.3148/cjdpr-2022-035

Influence of the 2019 Canada’s Food Guide on the Food Environment in Childcare and Early Learning Centres

2023· article· en· W4323037212 on OpenAlexaffvenueabout
Imene Hank, Melissa D. Rossiter, Sarah L. Finch

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2023
Typearticle
Languageen
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsAffect (linguistics)Food frequency questionnairePsychologyBusinessEnvironmental healthMarketingMedical educationMedicine

Abstract

fetched live from OpenAlex

Purpose: This study investigated the potential influence of the 2019 Canada’s Food Guide (CFG) on the eating environment and food provided in early learning and childcare centres across Canada. Methods: Directors of childcare centres were invited to complete an online survey about their awareness and adoption of the 2019 CFG and submit their menus for analysis. Results: Twenty-five directors completed the survey, and eighteen cycle menus were analyzed. Frequency and the types of foods offered in childcare centres were assessed. Ninety-two percent reported being aware of the changes in the food guide. Many challenges including the lack of support and resources, cost of food, and food reluctance could affect their ability to apply the changes, especially the incorporation of plant-based protein and the uncertainty around the amount of dairy products to provide. Menu analysis indicated frequency of offering items from the various food groups. Vegetables were mostly offered during lunchtime with an average offering rate of 4.83 ± 0.24 times per week. Conclusion: Representatives of early learning and childcare centres identified having difficulties in interpreting and applying the changes in the 2019 CFG. Dietitians have the knowledge and skills required to support childcare centres through training opportunities, workshops, toolkits, and advocacy.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.186
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.048
GPT teacher head0.329
Teacher spread0.281 · 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
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Dietetic Practice and ResearchSame topicNutrition, Health and Food BehaviorFrench-language works237,207