COVID-19 School Re-opening Plans: Rolling Back School Food Programming in Canada?
Bibliographic record
Abstract
At the beginning of 2020 national school food programs reached more children than any time in history making school food programs the most extensive form of social safety nets in the world. Looking to Canada, school food programs across the country serve more than 1 million students and provide multifaceted benefits including access to healthy fresh food choices, improving learning capacities, promoting nutritional awareness, assisting food-insecure households, and promoting local food procurement. However, since the beginning of the SARS-Cov 2 (COVID-19) pandemic these programs have faced operational challenges resulting in many rolling back their operations while food insecurity rates in Canada have increased dramatically. Framed as a Canadian case study analysis, this paper considers the discursive effects of provincial and territorial school reopening plans and the material consequences felt by SFPs. Specifically, this paper considers the reach, effectiveness, adoption, implementation, and maintenance of provincial and territorial school food programs within the broader conceptualization of ecological public health to consider if these programs were enabled or constrained by school reopening plans. The authors conducted a policy analysis of 57 primary and 164 supportive school reopening documents developed between April 2020 and September 2021. It was found that provincial and territorial school reopening plans primarily focused on measures to limit infectious disease transmission while food discussed in broad terms demonstrated policy makers' limited awareness of the important role of school food programs and support required to maintain them. In turn, two key observations were made: 1) government school reopening plans have overlooked the benefits of school food programs in Canada, and 2) school reopening plan designers missed opportunities to improve school food programs. This paper argues a thorough understanding of the impacts to school food programs by provincial and territorial COVID-19 public health guidelines is needed for politicians, policymakers, and school food practitioners to support the short- and long-term capacity of these programs and to ensure food insecurity and nutritional health issues in Canada continue to be on thepolitical agenda.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".