Diet Quality and Meal Patterns of Canadians: Evidence from the 2015 Canadian Community Health Survey, Nutrition
Bibliographic record
Abstract
Unhealthy dietary behaviors are recognized as a leading modifiable risk factors for non-communicable diseases. This study examined the diet quality and meal patterns of Canadian, using nationally representative nutrition data: The Canadian Community Health Survey, Nutrition (CCHS) 2015. Applying Health Canada’s Surveillance Tool, Tier System to categorize intakes, revealed Canadians are not meeting the recommendations outlined in Eating Well with Canada’s Food Guide (EWCFG) 2007. Almost a quarter of daily calories for some DRI groups (heavily impacted by high-fat and high-sugar foods) originated from Tier 4 and “other” foods not recommended in EWCFG. Canadian intakes occurred predominantly within the home and most were from foods that required no preparation and were “ready-to-eat”. These foods are poor in nutritional quality, high in sodium, saturated fats, and sugars. Evidence from this research supports action to reformulate Canadian foods and require front-of- pack labelling to reduce intakes of nutrients of public health concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".