Influence of Cooking Workshops on Cooking Skills and Knowledge among Children Attending Summer Day Camps
Bibliographic record
Abstract
This study aimed to measure the influence of the Chefs in Action program (3 cooking workshops) on cooking skills, nutrition knowledge, and attitudes towards healthy eating in children attending summer day camps and compare it with a single cooking workshop. Groups of children (8–12 years) were randomly assigned to the intervention group (n = 25) or to 1 of 3 comparison groups performing a single workshop (group 1, n = 16; group 2, n = 36; group 3, n = 24). Two dietitians evaluated cooking skills during the workshops. Nutrition knowledge and attitudes towards healthy eating were assessed before and after the intervention. No improvement in cooking skills was observed in the intervention group (P = 0.25). The intervention group’s cooking skills score was significantly higher than comparison group 1 (P < 0.001). Nutrition knowledge was significantly improved in the intervention group and the comparison group 3 (P < 0.0001) but no effect on attitudes towards healthy eating was observed (P group × time = 0.36). In conclusion, the Chefs in Action program positively impacted nutrition knowledge in children. The results also suggest that the type of recipe may influence nutrition knowledge and cooking skills. Further studies are needed to better assess the degree of difficulty required in cooking workshop recipes to improve cooking skills in children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".