The Health Canada Surveillance Tool could be an effective method for assessing alignment with 2019 Canada's Food Guide
Bibliographic record
Abstract
Abstract The Health Canada Surveillance Tool (HCST), a Canadian nutrient profile (NP) model, assesses products’ adherence to the 2007 Canada's Food Guide (CFG), using thresholds for total fat, saturated fat, sugars and sodium. In 2019, new dietary guidelines were published (i.e., CFG 2019); however; the HCST has not been updated to reflect changes implemented in this new guide. Given suggestions to adapt previously validated NP models rather than create new models, this research aimed to assess whether the HCST could be a useful tool to assess alignment with updated dietary guidance. Specifically, the objective of this study was to test the agreement between products’ alignment with the CFG 2007 (as per the HCST) and products’ alignment with the recently released CFG 2019 guidelines. This study analyzed data from the University of Toronto Food Label Information Program (FLIP) 2017 database. FLIP contains label and nutrition information for prepackaged food products from top Canadian grocery retailers. Products were categorized into Tiers based on HCST thresholds: Tiers 1 and 2 were considered “in line” with dietary guidance, while Tiers 3, 4 and “Other” (i.e. foods not addressed by CFG) were considered “not in line”. Two raters independently classified foods according to their alignment to CFG 2019. Proportions of products that were considered “in line” with CFG 2007 and 2019 were calculated. Overall agreement between alignment with CFG 2007 and 2019 was determined by cross-classifications of the proportion of products considered “in line” or “not in line” with both CFG versions. Cohen's Kappa (κ) statistic tested the level of agreement (Interpretation of κ: 0.01–0.20, “slight”; 0.21–0.40, “fair”; 0.41–0.60, “moderate”; 0.61–0.80, “substantial”; and 0.81–0.99, “almost perfect”). Analyses were conducted overall and by Health Canada's Table of Reference Amounts for Food category. In total, n = 16,973 products were analyzed, with 98% inter-rater reliability for CFG 2019 alignment. Overall, 30.2% and 28.2% of products were “in line” with CFG 2007 and 2019, respectively, with 80.4% overall agreement and “moderate” kappa agreement (κ [95% CI]: 0.49 [0.46, 0.49]). Overall agreement in individual food categories ranged from 100% (Dessert Toppings, Sauces, Sugars and Sweets; κ: N/A) to 54.8% (Eggs, κ: 0.21 [-0.01, 0.4]). From these results, the HCST appears to be an effective NP model for assessing alignment with CFG 2019. Further analysis could elucidate specific areas for adaptation of the HCST to optimize its functionality in this context.
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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 | 0.041 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".