The Impact of School Gardening on Cree Children's Knowledge and Attitudes toward Vegetables and Fruit
Bibliographic record
Abstract
PURPOSE: School-based interventions may increase children's preferences for vegetables and fruit (V&F). This Canadian study measured changes in Indigenous First Nations schoolchildren's V&F knowledge, preferences, and home consumption following the implementation of a gardening and V&F snack program. METHODS: At baseline, 7 months, and 18 months, children in grades 1-6 (i) listed at least 5 V&F they knew, (ii) tasted and indicated their preferences towards 9 vegetables and 8 fruit using a 6-point Likert scale, and (iii) indicated their home consumption of 17 V&F. RESULTS: At all 3 time points, 56.8% (n = 66/116) of children provided data. Children listed a greater number of V&F at 18 months (4.9 ± 0.1) than at baseline (4.5 ± 1.0) or 7 months (4.7 ± .07) (F(1.6,105.6) = 6.225, P < 0.05). Vegetable preferences became more positive between baseline (37.9 ± 9.3) and 7 months (39.9 ± 9.2), but returned to baseline levels at 18 months (37.3 ± 8.7) (F(1.6,105.8) = 4.581, P < 0.05). Fruit preferences at 18 months (42.7 ± 3.0) were greater than at baseline (41.1 ± 4.3) and at 7 months (41.9 ± 5.1) (F(1.7,113.3) = 3.409, P < 0.05). No change in V&F consumption occurred at home. CONCLUSIONS: Despite improvements in V&F knowledge and preferences, home consumption of V&F did not occur. Complementing school-based programs with home-based components may be needed to influence V&F intake of 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 | 0.002 |
| 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 | 0.000 | 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".