A Systematic Review of School Feeding Program in Canada and India: Perspectives for Mutual Improvement
Bibliographic record
Abstract
ABSTRACT Food and nutrition are essential for children’s physical and cognitive development, yet malnutrition affects millions globally, hindering their learning ability. School meal programs improve education, health, and nutrition, but Canada remains the only G7 country without a national program. It relies on diverse regional initiatives with varying funding models and delivery methods, leading to inconsistent outcomes. Canada is in the process of formulating a National School Food Policy. The paper aims to examine the School Feeding Program (SFP) in Canada and India’s Mid-Day Meal Program (MDMP) to identify replicable features and provide evidence-based recommendations for developing a national school feeding program in Canada. A Systematic literature review was carried out to analyze the current state of Canada’s school feeding program. The review also examined India’s Mid-Day Meal Scheme, its objectives, policies, and implementation strategies. Analysis of the selected literature revealed several key themes, including the relationship between nutrition and children’s well-being, the status of the School Feeding Program in Canada, barriers to establishing a national school food program, and the critical need for such an initiative. Additionally, the discussion highlights replicable insights from India’s Mid-Day Meal Scheme that could be modified and replicated in the National School Feeding Program in Canada. Keywords: School feeding program, Canada, Mid-Day Meal Scheme, India, Food Insecurity
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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 | 0.049 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.021 | 0.032 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".