An Experiential Cooking and Nutrition Education Program Increases Cooking Self-Efficacy and Vegetable Consumption in Children in Grades 3–8
Bibliographic record
Abstract
OBJECTIVE: Evaluate the effect of a community-based, experiential cooking and nutrition education program on consumption of fruits and vegetables and associated intermediate outcomes in students from low-income families. DESIGN: Quasi-experimental program evaluation by pre-post survey of participating students and their parents. SETTING: Underserved elementary and middle schools in Chicago. PARTICIPANTS: Students (n = 271; 65% girls, 44% Hispanic, 32% African American; 94% eligible for free/reduced price lunch) in grades 3-8 selected by school staff to participate by variable inclusion criteria. 59% of students who applied returned both pre- and post-surveys. INTERVENTION(S): Ten-week (2 h/wk) chef-instructor-led program held in cafeteria kitchens after school. MAIN OUTCOME MEASURE(S): Changes in student nutrition knowledge, cooking self-efficacy, fruit and vegetable liking and consumption, and communication to family about healthy eating. ANALYSIS: Changes from beginning to end of program were analyzed with paired t test. Results were considered significant at P < .05. RESULTS: Increased nutrition knowledge score from 0.6 to 0.8, cooking self-efficacy score from 3.2 to 3.6, and vegetable consumption score from 2.2 to 2.4 (all P < .05). Increased score for communication about healthy eating (4.1 to 4.4; P < .05) 6 months after the end of the course. CONCLUSIONS AND IMPLICATIONS: Experiential cooking and nutrition education programs led by chef-instructors may be effective ways to improve nutrition in low-income communities.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".