Association between whole grain food intake in Canada and nutrient intake, food group intake and diet quality: Findings from the 2015 Canadian Community Health Survey
Bibliographic record
Abstract
Whole grains have been associated with numerous beneficial health outcomes and are recommended in Canada's Food Guide; however, there is little research on whole grains specific to Canada. Therefore, the objective of this study was to characterize the association between Canadians' WG intake and nutrients, food groups and diet quality and to understand top sources of WG in the diets of Canadians. We used data from the Canadian Community Health Survey 2015: a cross-sectional survey that collected information on diet (using a 24-hour recall) and health from 20,487 Canadians 1 year and older. We classified study participants according to their WG intake: non-WG (n = 10,883) and three groups based on age-specific tertiles of WG intake, low-WG (n = 3,322), mid-WG (n = 3,180), and high-WG (n = 3,102). Results were analyzed using population-based survey methods and were adjusted for energy, age, gender, overweight/obesity, income, and supplement use. We found differences in nutrients and food groups by WG group: there was a significant linear trend across groups of increasing WG for increased fiber (children and adults), vitamin B6 (children), thiamin (adults), potassium (children and adults), zinc (adults), calcium (children and adults), iron (children and adults), magnesium (children and adults), fruit (adults), and legumes, nuts and seeds (adults); and decreased total fat (adults), saturated fat (adults), folate (children and adults), refined grains (adults and children), and meat and poultry (adults) intake. We found that there were no differences in total sugar or sodium intake across WG intake groups. The high WG intake group for both children and adults had higher diet quality, measured by the Nutrient Rich Food Index 9.3, compared to non-WG eaters. The top 2 food sources of WG across WG intake groups for children and adults were whole grain oat and high fiber breakfast cereal and whole grain and whole wheat bread. Other top sources of WG included rice, bread products, other breakfast cereals, salty snacks, cereal grains and flours, pasta, and sweet snacks. This research supports recommendations to increase WG foods intake as a means to improve diet quality of Canadians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".