Family meals can help children reach their 5 A Day: a cross-sectional survey of children's dietary intake from London primary schools
Bibliographic record
Abstract
BACKGROUND: This study aims to explore how the home food environment and parental attitudes and values affect children's fruit and vegetable (F&V) intake. METHODS: The sample consists of 2383 children with a mean age of 8.3 years (95% CI 8.2 to 8.3) attending 52 primary schools in London. These children are taking part in two randomised controlled trials to evaluate a school gardening programme. Diet was assessed using a validated 24-h food tick list, the Child And Diet Evaluation Tool (CADET). RESULTS: The CADET tool found that children consumed on average 293 g F&V (95% CI 287 to 303) per day. Clustered (by school) multilevel regression models with total F&V as the primary outcome were conducted to explore how the home environment affects children's F&V intake. Children of families who reported 'always' eating a family meal together at a table had 125 g (95% CI 92 to 157; p=<0.001) more F&V than families who never ate a meal together. Daily consumption of F&V by parents was associated with higher F&V (88 g, 95% CI 37 to 138) intake in children compared with rarely/never consumption of F&V by parents. Cutting up fruit and vegetables for children was associated with higher consumption. Families who reported always cutting up F&V for their children had 44 g (95% CI 18 to 71) more F&V than families who never cut up F&V. CONCLUSIONS: This study identified that cutting up F&V and family consumption of F&V facilitates children's intake. Eating a family meal together regularly could increase children's F&V intake and help them achieve the recommended intake. TRIAL REGISTRATION: ISRCTN11396528.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".