<i>Supporting Healthy Eating Among Preschoolers</i>: Challenges for Child Care Staff
Bibliographic record
Abstract
PURPOSE: The child care setting can help preschoolers develop healthy eating habits. Establishing such habits may increase preschoolers' likelihood of carrying them into adulthood, which can decrease the risk of nutrition-related chronic diseases. Challenges in supporting preschoolers' healthy eating were investigated among child care staff. METHODS: Three focus group interviews were conducted with 29 child care staff members. Audiotapes of the sessions were transcribed. RESULTS: Several themes were identified from the analysis of the transcripts. An intrapersonal (individual) factor was children's picky eating. Interpersonal factors (interactions) included perceptions that parents do not encourage their children to eat in a healthy way, and that child care staff's use of practices were inconsistent with health professional recommendations. Physical environment factors included perceptions that healthy food was not accessible at child care centres and that children have unhealthy food at home. CONCLUSIONS: Program planners and health professionals can develop and implement strategies to overcome some of the identified challenges to supporting preschoolers' 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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".