An examination of contributions of animal- and plant-based dietary patterns on the nutrient quality of diets of adult Canadians
Bibliographic record
Abstract
Dietary guidance and Canada’s 2019 Food Guide encourage increased consumption of plant-based foods as a source of dietary protein. However, there is an absence of recent data on protein and nutrient intakes and quality of Canadian dietary patterns that might occur with increased plant protein intakes. This study compared food sources and nutrient intakes of Canadian adults within groups of increasing plant protein-containing diets. The CCHS 2015 Public-Use Microdata File of single 24-hour dietary recalls of males and females ≥19 years (n = 6498) or ≥70 years (n = 1482) were examined. Respondents were allocated into 4 groups defined by their protein intake percentage coming from plant-based foods (i.e., group 1: 0–24.9%, group 2: 25–49.9%, group 3: 50–74.9%, group 4: 75–100%). Protein intake in adults averaged 63.3% animal and 36.7% plant protein. Where plant protein contributed >50% protein, higher intakes of carbohydrate, dietary fibre, folate, dietary folate equivalents, iron and magnesium (p < 0.001) but lower intakes of total and saturated fat, protein, vitamin D, vitamin B12, riboflavin and niacin (p < 0.0001) were reported. In contrast, group 1 had higher total and saturated fat, protein, vitamin B12, thiamin, niacin, and zinc, but lower carbohydrate, dietary fibre, and magnesium. Balancing plant- with animal-based protein foods leads to healthier dietary patterns with more favourable nutritional properties when compared with diets based on either high animal or high plant protein content. Novelty: Combinations of animal- and plant-based proteins improve nutrient quality of Canadian diets. The source of protein influences diet quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".