Fruit and Vegetable Lesson Plan Pilot Intervention for Grade 5 Students from Southwestern Ontario
Bibliographic record
Abstract
The purpose was to create and assess the impact of food literacy curriculum alongside a centrally procured school snack program among grade five students in Southwestern Ontario, Canada. Grade five students (N = 287) from five intervention and three controls schools participated in an 8-week food delivery program. In addition to the food delivery program, intervention schools received a resource kit and access to 42 multidisciplinary food literacy lesson plans using the produce delivered as part of the food delivery program. Participants completed matched pre- and post-test online surveys to assess fruit and vegetable intake, knowledge, preferences, and attitudes. Descriptive analyses and changes in scores between the intervention and control schools were assessed using one-way ANOVAs, paired samples t-tests, and McNemar’s tests. In total, there were 220 participants that completed both the pre- and post-test surveys. There was a significant improvement in fruit and vegetable intake (p = 0.038), yet no differences in knowledge of the recommended number of food group servings, knowledge of food groups, or fruit and vegetable preferences or attitudes were observed. Integrating nutrition lesson plans within core curricula classes (e.g., math, science, and literacy) can lead to modest increases in fruit and vegetable intake.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".