“A few more bites?”: Manifestation of pressure‐to‐eat in child care
Bibliographic record
Abstract
AIMS: Pressuring children to eat can override hunger and satiety cues, which may lead to over- or under-eating and food refusal. This study aims to describe the manifestations of pressure-to-eat in child care from early childhood educators. METHODS: A secondary data analysis was conducted using qualitative content analysis. Observations of educators from child care centres in Nova Scotia and Prince Edward Island (n = 9) occurred over 2 days. Observation data were coded and counted to determine the most and least prevalent forms of pressure. The count results were then assessed quantitatively by educator demographic characteristics to explore potential associations using nonparametric tests (Mann-Whitney U test, Spearman's correlations). RESULTS: Offering food and encouraging eating without referencing hunger or satiety was found to be the most common type of pressure; serving children without asking if they were hungry made up the majority of this type of pressure. This was less common with both increasing educator age (r = -0.692, p = 0.039), as well as years of experience (r = 0.878, p = 0.002). Pressuring children to eat by referring to health benefits and consequences was the least common type of pressure. CONCLUSION: This study provides insight into the types and frequency of pressure-to-eat strategies implemented in child care centres, which can inform interventions to create more responsive feeding environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".