Impact of the COVID-19 pandemic on the adaptability and resiliency of school food programs across Canada
Bibliographic record
Abstract
Introduction: Following the sudden closure of schools due to the pandemic in 2020, many school food program (SFP) operators lost their operating venues and had to innovate to continue distributing meals to children. Our objective was to assess the impact of the COVID-19 pandemic on the delivery, adaptability, and resiliency of school food programs across Canada by conducting a systematic rapid review. Materials and methods: Systematic literature searches identified newspaper articles and social media sources related to the adaptations and challenges faced by school food programs across Canada in response to the COVID-19 pandemic. Included sources were assessed and thematically categorized according to the dimensions of the Analysis Grid for Environments Linked to Obesity (ANGELO) and Getting To Equity (GTE) frameworks to identify factors impacting the delivery, adaptability, and resiliency of school food programs in Canada. Results: School food programs in Canada made various efforts to meet existing and new challenges associated with the delivery of these programs to keep feeding school children, particularly those most vulnerable, during the pandemic. Distribution of food kits, prepared meals and gift cards/coupons were successful pathways in ensuring support for food accessibility to students and their families. Increased collaborations between community members and organizations/stakeholders to help maintain food delivery or collectively offer new modes to deliver foods were most frequently cited as key to facilitating school food programming. However, maintenance and sustainability related to operating costs and funding were identified as key challenges to successful school food programming. Conclusion: Our study highlights the swift and substantial transformation school food programs,, underwent in response to the pandemic, driven by the urgent need to ensure that students still had access to nutritious meals and the importance of policy and resource support to bolster the adaptability and resiliency of these programs. Findings on facilitators and challenges to school food programs during the early months of the COVID-19 pandemic can inform development of guidelines to design a robust national Canadian school food program and help make existing programs more sustainable, adaptable, and resilient.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".