Effectiveness of a School-Based Culinary Programme on 9- and 10-Year-Old Children’s Food Literacy and Vegetable, Fruit, and Breakfast Consumption
Bibliographic record
Abstract
School-based culinary courses may increase children’s food literacy and improve their eating behaviours. This study assessed the impact of a school-based culinary programme on 9- and 10-year-old students’ food literacy and vegetable, fruit, and breakfast consumption. This cluster quasi-experimental trial compared 88 grade 4 and 5 students who participated in the Apprenti en Action programme to 82 students who did not. Students’ food literacy and eating behaviours were assessed with a self-administered questionnaire. The programme’s impact on vegetable and fruit consumption, cooking skills, food skills, and food knowledge was measured using MANOVA, and the odds of eating breakfast at least five times per week were assessed with logistic regression. Students who participated in the programme reported a greater increase in their cooking skills (p = 0.013) and food knowledge (p = 0.028) than students in the control group. No effect was found on food skills and vegetables, fruit, and breakfast consumption (p-values > 0.05). Boys improved their cooking skills (p = 0.025) and food knowledge (p = 0.022), but girls did not. The programme improved students’ cooking skills and food knowledge, especially among boys; however, modifications are needed to improve students’ food skills and eating behaviours.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".