Exploring Perceptions and Needs of School Food Programs Among Parents and Caregivers in the Greater Toronto Area
Bibliographic record
Abstract
National policies for school food programs have been implemented in several countries and provide numerous benefits to children across the world. However, Canada is the only OECD country without a national school food program (SFP). As the enthusiasm to develop a national SFP grows, it is important to inform policymakers on ways to develop a national SFP that can meet the needs of diverse communities in Canada. To date, there are very few studies that explore the perceptions and needs of parents and caregivers regarding SFPs. Thus, we conducted a mixed-methods study involving focus groups/interviews and surveys with parents and caregivers in the Greater Toronto Area (GTA) to gain insight on preferred SFP attributes. We also took a closer look into the perceptions of ethnic-specific sub-groups (South Asian, East Asian, and Southeast Asian households) in the GTA to understand their perceptions on the cultural aspects of SFPs. Based on our findings, we present a framework that highlights attributes of a holistic and multicomponent SFP, that can inform policymakers on the most important objectives, attributes, and cultural considerations for a national SFP. In addition, we provide a methodological reflection that serves as a foundation to aid fellow researchers in advancing similar work with diverse communities across Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".