Assessing the potential for healthier consumer food substitutions in Canada: population-level differences in dietary intakes of whole grains, refined grains, red meats, and legumes
Bibliographic record
Abstract
The potential for healthier consumer food substitutions is an important factor in the study of food environments, dietary choices, and population nutrition promotion. The burden of diet-related non-communicable diseases is unevenly distributed across Canada and variation in the food environment sub-nationally may be an important explanation. We used population-based 24 h dietary recall data from the 2015 Canadian Community Health Survey—Nutrition for Canadian adults ( n = 13 919) to examine dietary intakes of two food group pairings (whole grains/refined grains and legumes/red meats) where consumer substitutions have been recognized to be of importance in promoting healthy and sustainable population diet. We used an ANOVA followed by pairwise comparisons with Bonferroni correction to estimate differences in intakes between provinces for daily weight and proportion of total energy consumed. Based on the Global Burden of Disease Study, Canadians consumed below the average daily requirements of legumes and whole grains and well above the required range of red meat, suggesting room for broad improvements to population diet. Findings also demonstrate that there is potential for targeted shifts in dietary intakes among non-consumers of certain foods (e.g., legumes). This study may inform intervention development for the consumer nutrition environment including food accessibility and affordability to reduce non-communicable disease risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.001 | 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".