School-Level Perspectives of the Ontario School Nutrition Program
Bibliographic record
Abstract
The purpose of this study was to assess the implementation of a school snack program for children in elementary schools. School-level program volunteers’ experiences were explored using semi-structured interviews. Fieldnotes were taken during on-site school visits. Quantitative data were collected through a General Information Form and Weekly Logbooks. Seven elementary schools in Southwestern Ontario were invited and agreed to participate. Interviews (n = 27) revealed that volunteers valued the program for its universality, the excitement it created, the opportunity for students to try new foods, and the social interactions that it generated. Challenges included the burden on snack volunteers to plan, procure, and prepare foods; the competition the program posed for school priorities; limited funding; and a lack of clear purpose for the program. Suggestions for improvement included providing adequate and sustained resources and an integration of the program into the curriculum. Data obtained from 15 on-site visits, 7 General Information Forms, and 59 (out of a total of 70) Weekly Logbooks confirmed the data obtained from interviews. This research provides insights into the challenges of volunteer-led school snack programs in Canada and may guide policy makers, practitioners, and researchers in the development of a universal, nationally funded school food program.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".