Art on a Plate: A Pilot Evaluation of an International Initiative Designed to Promote Consumption of Fruits and Vegetables by Children
Bibliographic record
Abstract
OBJECTIVE: To evaluate the 2016 International Chefs Day cooking workshops Art on a Plate. DESIGN: Nonexperimental pretest-posttest design SETTING: Art on a Plate workshops with children from 14 countries in Asia, America, and Europe. PARTICIPANTS: A total of 433 workshop participants aged 4-14 years (mean age, 8.6 years). INTERVENTION: Instructed by a chef, children in the workshops created a self-chosen design on their plate with a spinach-fruit salad. MAIN OUTCOME MEASURES: Before and after the workshop, a questionnaire assessing liking and willingness to eat or taste; hunger was assessed using the Teddy the Bear method and emotions were assessed using the Self-assessment Manikin. The event coordinator evaluated salad intake. ANALYSIS: Linear and generalized linear (logit) mixed models were used to test statistical differences before and after the workshop. RESULTS: The workshop resulted in a small increase in liking (n = 409; P = .02) and person control (n = 375; P < .001) and a decrease in hunger (n = 379; P < .001). A total of 30% of children increased their liking scores, 18% decreased them, and 52% did not change them. Significant associations of liking and change in liking with salad intake were in the expected direction. CONCLUSION AND IMPLICATIONS: This study showed the positive effect of a cooking workshop on children's salad liking across a selection of countries worldwide. Further research and novel methods are needed to evaluate the long-term effectiveness of cooking activities in real-life settings across countries.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".