Impact of Child Interaction With Food Preparation on Vegetable Preferences: A Farm-Based Education Approach
Bibliographic record
Abstract
OBJECTIVE: To identify the impact of child involvement in vegetable preparation on vegetable preference and attitudes toward eating vegetables. DESIGN: Pre-post mixed-methods. SETTING: Food and Farming Camp at a nonprofit urban farm in Sunnyvale, California. PARTICIPANTS: Camp participants aged 8-10 years (n = 34 girls, n = 12 boys), and aged 11-14 years (n = 19 girls, n = 4 boys). INTERVENTIONS: Involvement in vegetable preparation through harvesting, cutting, cooking, and seasoning before consumption. Interviews identified camper perception of vegetable preference and involvement in preparation. MAIN OUTCOME MEASURES: Change in vegetable preference from baseline with and without involvement in vegetable preparation. Attitudes toward involvement in vegetable preparation. ANALYSIS: Adjustment of preferences to baseline followed by tests of hypotheses to identify differences with involvement. Thematic, qualitative coding to identify prevalent themes within interview responses. RESULTS: Younger campers preferred vegetables they prepared (P < 0.05), except for carrots. Campers were more likely to choose vegetables they prepared (P < 0.05). Campers of both age groups were curious to try their vegetable creations and described feelings of pride and responsibility related to preparing vegetables. CONCLUSIONS AND IMPLICATIONS: Involvement with food preparation, in particular in a garden-based setting, may provide an accessible method to improve child vegetable preference.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".