Bibliographic record
Abstract
We investigated the impact of the Kids' Shop Smart Tour program on participants' attitudes toward trying new foods and eating a variety of foods, as well as their recognition of Canada's Food Guide to Healthy Eating. Data were collected from parents/caregivers, students in kindergarten to grade 3, and teachers; questionnaires, quizzes, and interviews were used. Questionnaires were sent home with 947 students; 52% of parents/caregivers returned completed questionnaires. Many parents/caregivers reported that their children tried and liked unfamiliar foods on the tour. No significant difference was detected in children's willingness to try new foods or consumption of a greater variety of food before and after the tour. Quiz score differences between participants and a comparison group were not statistically significant. Of the 38 teachers who completed interviews, 97% reported that the program helped them meet curriculum requirements; 95% would recommend the resource to other teachers. Quantitative findings do not indicate that the program increases children's willingness to try new foods or eat a greater variety of food. However, qualitative data revealed that some parents observed their children trying new foods more willingly and demonstrating greater knowledge of and interest in Canada's Food Guide to Healthy Eating. Further research with validated measurement tools is recommended to establish the effectiveness of the Kids' Shop Smart Tour.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".