Assessing the nutritional quality of diets of Canadian children and adolescents using the 2014 Health Canada Surveillance Tool Tier System
Bibliographic record
Abstract
BACKGROUND: Health Canada's Surveillance Tool (HCST) Tier System was developed in 2014 with the aim of assessing the adherence of dietary intakes with Eating Well with Canada's Food Guide (EWCFG). HCST uses a Tier system to categorize all foods into one of four Tiers based on thresholds for total fat, saturated fat, sodium, and sugar, with Tier 4 reflecting the unhealthiest and Tier 1 the healthiest foods. This study presents the first application of the HCST to examine (i) the dietary patterns of Canadian children, and (ii) the applicability and relevance of HCST as a measure of diet quality. METHODS: Data were from the nationally-representative, cross-sectional Canadian Community Health Survey 2.2. A total of 13,749 participants aged 2-18 years who had complete lifestyle and 24-hour dietary recall data were examined. RESULTS: Dietary patterns of Canadian children and adolescents demonstrated a high prevalence of Tier 4 foods within the sub-groups of processed meats and potatoes. On average, 23-31 % of daily calories were derived from "other" foods and beverages not recommended in EWCFG. However, the majority of food choices fell within the Tier 2 and 3 classifications due to lenient criteria used by the HCST for classifying foods. Adherence to the recommendations presented in the HCST was associated with closer compliance to meeting nutrient Dietary Reference Intake recommendations, however it did not relate to reduced obesity as assessed by body mass index (p > 0.05). CONCLUSIONS: EWCFG recommendations are currently not being met by most children and adolescents. Future nutrient profiling systems need to incorporate both positive and negative nutrients and an overall score. In addition, a wider range of nutrient thresholds should be considered for HCST to better capture product differences, prevent categorization of most foods as Tiers 2-3 and provide incentives for product reformulation.
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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 | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".