Characteristics of Canadian school food programs funded by provinces and territories
Bibliographic record
Abstract
Given the complexity of school food programs (SFPs) in Canada and recent political developments, this research provides a systematic examination of provincially and territorially-funded SFPs during the 2018/19 school year. Relevant literature and the RE-AIM Framework, a planning and evaluation tool developed by Glasgow, Boles & Vogt (1999), informed the development of an electronic survey sent to leads in each province and territory to assess SFP Reach, Effectiveness, Adoption, Implementation, and Maintenance. Results from 24 surveys (16 from provincial/territorial ministries/ departments, supplemented by surveys from 8 non-governmental organizations (NGOs), indicate considerable variability across Canada. Collectively, provinces and territories contributed over $93 million to support a minimum of 6,159 programs in 5,186 JK-12 schools, funding free breakfasts, snacks, and/or lunches for a minimum of 1,018,323 or 20% of students (based on limited data in some jurisdictions). The majority of provinces and territories partner with one or more NGOs and rely heavily on NGO staff and volunteers. Program demand often exceeds supply and monitoring is inconsistent. This research, which provide an important but incomplete picture of SFPs in Canada, indicate the value of future discussions about SFP administration, especially about program mandates, student reach and universality, sustainability and resources, and monitoring based on nationally-harmonized metrics. The results offer opportunities to explore promising organizational practices, enhanced collaboration, and sharing of expertise, all of which would assist with developing the National School Food Program proposed in the 2019 federal budget.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".