Evaluating Diet Quality of Canadian Adults Using Health Canada’s Surveillance Tool Tier System: Findings from the 2015 Canadian Community Health Survey-Nutrition
Bibliographic record
Abstract
The 2014 Health Canada’s Surveillance Tool, Tier System (HCST) is a nutrient profiling model developed to evaluate adherence of food choices to dietary recommendations. With the recent release of the nationally representative Canadian Community Health Survey-Nutrition (CCHS-N) 2015, this study used HCST to evaluate nutritional quality of the dietary intakes of Canadians in the CCHS-N. Dietary intakes were ascertained using 24-hour dietary recalls from Canadians adults ≥19 years (N = 13,605). Foods were categorized into four Tiers based on degree of adherence to dietary recommendations according to thresholds for sodium, total fat, saturated fats, and sugars. Tier 1 and Tier 2 represented “recommended foods”, Tier 3 represents foods to “choose less often”, and Tier 4 represented foods “not recommended”. Across all dietary reference intakes (DRI) groups, most foods were categorized as Tier 1 for Vegetable and Fruits (2.2–3.8 servings/day), Tier 2 for Grain Products (2.9–3.4 servings/day), Tier 3 for Milk and Alternatives (0.7–1 serving/day) or for Meat and Alternatives (1.1–1.6 servings/day). Consumption of foods from Tier 4 and “other foods” such as high fat/sugary foods, sugar-sweetened beverages, and alcohol, represented 24–26% and 21–23% kcal/day, for males and females, respectively. Canadians are eating more foods categorized as Tier 1–3, rather than Tier 4. Adults with the highest intakes of Tier 4 and “other foods” had lower intakes of macronutrients and increased body mass index. These findings can be used by policy makers to assist in identifying targets for food reformulation at the nutrient level and quantitative guidance to support 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".