A Nutrition Report Card on food environments for children and youth: 5 years of experience from Canada
Bibliographic record
Abstract
OBJECTIVE: In 2014, a Nutrition Report Card (NRC) was developed as a sustainable, low-cost framework to assess the healthfulness of children's food environments and highlight action to support healthy eating. We summarise our experiences in producing, disseminating, evaluating and refining an annual NRC in a Canadian province from 2015 to 2019. DESIGN: To produce the NRC, children's food environment indicator data are collected, analyzed and compiled for consensus grading by an Expert Working Group of researchers and practitioners. Knowledge translation activities are tailored annually to the needs of target audiences: researchers, practitioners, policymakers and the public. Evaluation of reach is conducted through diverse strategies, including tracking media coverage and website traffic. Assessment of impact on diets and health outcomes is planned. SETTING: Alberta, Canada. PARTICIPANTS: Not applicable. DISCUSSION: The grading process has facilitated refining the NRC to enhance its relevance and utility as a tool for its target audiences. Its public release consistently captures media interest and policymakers' attention. The importance of partnerships in revealing data sources and in strategising to enhance policy approaches to improve food environments is apparent. The NRC has benchmarked progress and stimulated dialogue regarding healthy food environments for children. CONCLUSIONS: The NRC may help to foster a supportive climate for improving the quality of children's food environments. As an engaging and accessible document, the NRC represents a key mechanism for collating data related to children's food environments and ensuring it reaches the audiences best positioned to use it. Efforts are underway to expand the NRC 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".