Protein consumption in Canadian habitual diets: usual intake, inadequacy, and the contribution of animal- and plant-based foods to nutrient intakes
Bibliographic record
Abstract
The 2019 Canada’s Food Guide (CFG) emphasizes consumption of plant protein with implications for protein adequacy and nutrient intakes, yet a baseline with which to compare future dietary trends that may result from its adoption is not available. The objectives were to assess usual protein intake, inadequacy, and the contribution of animal- and plant-based foods to intake of protein, nutrients, and energy in Canada. Twenty-four-hour dietary recalls from the 2015 Canadian Community Health Survey – Nutrition were used to assess dietary intake among adults (n = 13 616). The National Cancer Institute method was used to estimate usual protein intake and inadequacy. Population ratios were used to determine the contribution of animal- and plant-based foods to intake of protein, nutrients, and energy. Usual protein intake averaged 79.47 ± 0.70 g/d; inadequacy was highest for females ≥71 y (9.76 ± 2.04%). Top protein contributors were red and processed meat (21.6 ± 0.55%), poultry and eggs (20.1 ± 0.81%), cereals, grains, and breads (19.5 ± 0.31%), and dairy (16.7 ± 0.38%). Dairy contributed most to calcium (53.4 ± 0.61%), vitamin D (38.7 ± 1.01%), but also saturated fat (40.6 ± 0.69%), whereas cereals, grains, and breads contributed most to iron (46.5 ± 0.57%) and vegetables and fruit to potassium (32 ± 0.45%). Given that animal sources contributed overwhelmingly to protein intake in 2015, dietary shifts towards plant protein needed to meet the 2019 CFG recommendations may pose a challenge, particularly for populations most at risk of inadequacy. Novelty: Older adults and females are most at risk of not meeting protein recommendations. Animal sources contribute two-thirds of the protein consumed by Canadian adults.
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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 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".