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Record W4293089755 · doi:10.3148/cjdpr-2022-015

Family Feeding Practices of Parents on Prince Edward Island: A Focus on Responsive Feeding

2022· article· en· W4293089755 on OpenAlexaffvenueabout
Katrina A. Nagge, Sarah L. Finch, 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 Island
Fundersnot available
KeywordsFocus (optics)MedicineGerontologyDemographySociology

Abstract

fetched live from OpenAlex

The home environment and parental influence are strong predictors of eating behaviours in young children and can influence healthy development. The objective of this study was to describe the feeding practices of a sample of families on Prince Edward Island. Eleven parent participants were recruited, one-on-one interviews were held, and a thematic analysis was conducted. Conversations with parents revealed that the family feeding environment is influenced by a multitude of factors that change daily and need to be navigated based on the age of the child. Parents saw family meals as time together and an opportunity to model healthy eating behaviours; however, they faced several challenges at mealtimes, including perceived picky eating. Parents recognized their children's hunger and satiety cues, although they respected satiety signals more often if children ate what they perceived as a lot of food. Many parents used food as a reward to encourage their children to eat more but recognized that it could lead to the development of undesirable habits. Despite the complex factors that influence feeding, dietitians can work with families to foster a responsive feeding environment by encouraging family meals, recognizing and respecting hunger and satiety cues, and understanding typical changes in eating behaviours as children age.

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)0.002
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 score0.860
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.102
GPT teacher head0.412
Teacher spread0.310 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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