Healthy food procurement and nutrition standards in public facilities: evidence synthesis and consensus policy recommendations
Bibliographic record
Abstract
INTRODUCTION: Unhealthy foods are widely available in public settings across Canada, contributing to diet-related chronic diseases, such as obesity. This is a concern given that public facilities often provide a significant amount of food for consumption by vulnerable groups, including children and seniors. Healthy food procurement policies, which support procuring, distributing, selling, and/or serving healthier foods, have recently emerged as a promising strategy to counter this public health issue by increasing access to healthier foods. Although numerous Canadian health and scientific organizations have recommended such policies, they have not yet been broadly implemented in Canada. METHODS: To inform further policy action on healthy food procurement in a Canadian context, we: (1) conducted an evidence synthesis to assess the impact of healthy food procurement policies on health outcomes and sales, intake, and availability of healthier food, and (2) hosted a consensus conference in September 2014. The consensus conference invited experts with public health/nutrition policy research expertise, as well as health services and food services practitioner experience, to review evidence, share experiences, and develop a consensus statement/recommendations on healthy food procurement in Canada. RESULTS: Findings from the evidence synthesis and consensus recommendations for healthy food procurement in Canada are described. Specifically, we outline recommendations for governments, publicly funded institutions, decision-makers and professionals, citizens, and researchers. CONCLUSION: Implementation of healthy food procurement policies can increase Canadians' access to healthier foods as part of a broader vision for food policy in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.191 | 0.336 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".