Proposing a Framework for School Food Program Evaluation in Canada
Bibliographic record
Abstract
Healthy eating in school-aged children supports optimal growth and learning; however, diet quality and food insecurity are a source of concern for many school-aged children in Canada. Canadian school-aged children’s diets are a concern. In 2019 the Canadian federal government announced the intention to work towards a National School Food Program. A nationally organized program can evolve and meet the needs of children if there is a national evaluation strategy developed along with the program. A scoping review published in 2019 consisted of reports of school food programs in Canada evaluating nutritional impacts and food system sustainability. Food system sustainability recognizes the full impact that school food programs can have on individual, community, and environmental health by integrating social determinants of health, food systems, and economic sustainability. We conducted a content analysis of the evaluation strategies of these programs. Of the 17 peer-reviewed and 18 grey literature publications in the initial scoping review, 12 peer-reviewed and seven grey literature publications contained an evaluation component. Components assessed social determinants of health, including changes in food intake, knowledge about local foods, educational and behavioural outcomes, general knowledge, intention to eat, and willingness to try new foods. An evaluation template for school food programs including categories for social systems, environmental and economic sustainability would capture elements contributing to program impact.
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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 | 0.423 | 0.397 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.039 | 0.044 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.032 | 0.014 |
| Open science | 0.015 | 0.020 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".