Assessing the Nutritional Quality of Diets of Canadian Adults Using the 2014 Health Canada Surveillance Tool Tier System
Bibliographic record
Abstract
The 2014 Health Canada Surveillance Tool (HCST) was developed to assess adherence of dietary intakes with Canada's Food Guide. HCST classifies foods into one of four Tiers based on thresholds for sodium, total fat, saturated fat and sugar, with Tier 1 representing the healthiest and Tier 4 foods being the unhealthiest. This study presents the first application of HCST to assess (a) dietary patterns of Canadians; and (b) applicability of this tool as a measure of diet quality among 19,912 adult participants of Canadian Community Health Survey 2.2. Findings indicated that even though most of processed meats and potatoes were Tier 4, the majority of reported foods in general were categorized as Tiers 2 and 3 due to the adjustable lenient criteria used in HCST. Moving from the 1st to the 4th quartile of Tier 4 and "other" foods/beverages, there was a significant trend towards increased calories (1876 kcal vs. 2290 kcal) and "harmful" nutrients (e.g., sodium) as well as decreased "beneficial" nutrients. Compliance with the HCST was not associated with lower body mass index. Future nutrient profiling systems need to incorporate both "positive" and "negative" nutrients, an overall score and a wider range of nutrient thresholds to better capture food product differences.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".