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Record W4293089422 · doi:10.3148/cjdpr-2022-017

Comparison of Responsive Feeding Practices in Child Care and Home Environments in Nova Scotia

2022· article· en· W4293089422 on OpenAlexaffvenueabout
Jessie‐Lee D. McIsaac, Brenna Richard, Joan Turner, Melissa D. Rossiter

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2022
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsUniversity of Prince Edward IslandMount Saint Vincent University
Fundersnot available
KeywordsNova scotiaNova (rocket)MedicinePsychologyGerontologyGeographyEngineeringAeronauticsArchaeology

Abstract

fetched live from OpenAlex

Purpose: The values, beliefs and practices between the family home and child care environment can play a role in shaping a responsive food environment for young children, but few studies have explored the differences across these settings. The purpose of this study was to compare responsive feeding practices in child care and home environments through the framework of the 2019 Canada Food Guide healthy eating recommendations. Methods: Nova Scotia families and child care providers completed an online survey on responsive feeding. Independent-samples t-tests explored the differences between family and child care respondents on variables related to the 2019 Canada’s Food Guide, including: food variety, mindfulness, eating with others, cooking more often, and enjoyment of food. A directed content analysis was used to code the open-ended qualitative questions. Results: Family respondents (n = 603) were more likely to report offering a variety of foods, repeated exposures to new foods, and asking children about fullness. Child care respondents (n = 253) were more likely to sit with children during meals and less likely to encourage children to finish their food. Conclusions: The results identify potential points of intervention, including the importance of increasing communication to ensure mutually supportive messages and environments for healthy eating.

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)0.004
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 score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.116
GPT teacher head0.441
Teacher spread0.324 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

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