‘For me it’s just the conversation:’ responsive feeding influences among early childhood educators
Bibliographic record
Abstract
OBJECTIVE: Early learning and childcare (ELCC) programmes play an important role in shaping children's eating behaviours and long-term health by establishing a responsive feeding environment that encompasses not only mealtime behaviours but also extends to play activities and language used throughout the day. Despite their potential benefits, many ELCC centres do not consistently implement responsive feeding behaviours, facing challenges with organisational and behavioural changes within these environments. This study aims to identify influences on responsive feeding behaviours among early childhood educators prior to an intervention. DESIGN: A qualitative study guided by the Behaviour Change Wheel framework and Capability Opportunity Motivation - Behaviour (COM-B) model. Semi-structured interviews and focus groups were conducted, recorded and transcribed verbatim. Thematic analysis was employed to identify themes, categorising them within the corresponding COM-B domains. SETTING: Canada. PARTICIPANTS: Forty-one ELCC staff in various roles across eight centres from two provinces in eastern Canada. RESULTS: Fifteen influences, spanning across all six domains of the COM-B model, were identified, highlighting gaps in educators' knowledge and skills, varied approaches to food and feeding, and the interactions with children, parents, and co-workers on mealtimes dynamics. Additionally, costs, centre location and other physical resources emerged as enabling opportunities for responsive feeding behaviours. CONCLUSIONS: These findings offer a comprehensive exploration of the diverse factors influencing responsive feeding behaviours among educators, each varying in its potential for future behaviour change intervention.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".