Development and validity testing of the Canadian Food Scoring System (CFSS), a nutrient profile model based on the recommendations of Canada’s Food Guide 2019
Bibliographic record
Abstract
Canada’s food guide (CFG) 2019 provides dietary guidance for all Canadians; however, there is no tool available to help Canadians easily determine how individual foods align with CFG. Therefore, the objectives of this study were (1) to develop a nutrient profile model, Canadian Food Scoring System (CFSS), to rank the healthfulness of individual foods according to the recommendations of CFG; and (2) to assess its validity. The CFSS was developed based on CFG, leveraging existing Canadian labelling regulations to set quantitative criteria for the CFG recommendations. The CFSS included three main steps: (1) classifying foods into the nutritious food categories and assigning points based on the alignment with the recommendations of CFG; (2) deducting points based on the levels of saturated fat, sugars, and sodium using thresholds from Canadian front-of-pack labelling regulations; and (3) calculating the final score from the first two steps to classify foods into one of five categories: “very poor,” “poor,” “fair,” “good,” or “excellent” choice. Convergent validity was assessed by examining the alignment of the CFSS with Health Canada’s CFG-Food Classification System using a national food composition database and the Healthy Eating Food Index-2019 using nationally representative dietary intake survey data. The CFSS showed strong correlation with the CFG-Food Classification System (ρ = 0.782, p < 0.001) and moderate correlation with the Healthy Eating Food Index-2019 ( r = 0.636, p < 0.001), indicating good convergent validity both at the food and dietary level. The newly developed CFSS can assess the alignment of individual foods with CFG, which can be used to help Canadians more easily make healthy food choices.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".