Comparing how Canadian packaged food products align with the 2007 and 2019 versions of Canada’s Food Guide
Bibliographic record
Abstract
In 2019, Canada’s Food Guide (CFG) was updated from the 2007 version. This study developed a food-based nutrient profile model (NPM) to evaluate the alignment of packaged food and beverage products with CFG 2019 and compared it with CFG 2007. Packaged products from the University of Toronto’s Food Label Information Program 2017 database were evaluated in terms of their alignment with CFG 2007 (using the Health Canada Surveillance Tool (HCST)) and CFG 2019 (using our newly developed CFG 2019 NPM). Agreement in alignment (e.g., products “in line” according to CFG 2019 NPM and in Tiers 1 or 2 according to the HCST) was calculated and differences in alignment and reasons for differences were quantified and described. Overall agreement in product alignment between CFG 2007 and 2019 was 81.9%, with fewer products aligned with CFG 2019: 16.4% vs. 31.8%, (χ2 = 189.12, p < 0.001). Differences in alignment varied across food categories (0.0–73.8%), explained by differences in CFG 2019, reflected in the CFG 2019 NPM (e.g., emphasis on avoiding processed foods, encouraging whole grains and low-fat dairy). This study presents a first step in assessing packaged foods’ alignment with CFG 2019; future work is needed to evaluate broader dietary adherence to the updated recommendations. Novelty: A food-based nutrient profile model was developed based on the 2019 CFG and tested on packaged foods by comparing it with the nutrient-based HCST, based on CFG 2007. Most (82%) packaged products were “not in line” with either CFG version.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".