Association Between Dietary Protein Sources and Nutrient Intake in the Diet of Canadian Children
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Canada's 2019 Food Guide (CFG) encourages the increased consumption of plant-based foods as dietary protein sources. However, the nutritional implications of replacing animal-based proteins with plant-based alternatives in children's diets remain unclear. This study aimed to examine the association between protein food sources and nutrient intake in Canadian children aged 9-18 years. METHODS: We analyzed data from 2324 children from the 2015 Canadian Community Health Survey (CCHS), using the Public-Use Microdata File (PUMF) containing 24 h dietary recalls. Participants were categorized into four groups based on the proportion of protein from plant sources: Group 1 (0-24.9%), Group 2 (25-49.9%), Group 3 (50-74.9%), and Group 4 (75-100%). Nutrient intakes were compared and assessed against the Recommended Dietary Allowances (RDAs) and Adequate Intake (AI). RESULTS: Groups 1 and 3 had less favorable macronutrient profiles than Group 2. A 3:1 animal-to-plant protein ratio (Group 2) aligned most closely with dietary recommendations. Groups 1 and 2 exceeded RDAs for protein, iron, vitamin B12, thiamine, riboflavin, niacin, vitamin B6, and zinc by over 146% (about four SDs above the mean requirement), suggesting a low risk of inadequacy, although saturated fat intake was high. The intakes of vitamin D and folate were below 66% of the RDA, while calcium and magnesium were below 100% in some subgroups, with probabilities of inadequacy of 0.93 and 0.31, respectively. Group 4 (2.71%) was too small for reliable analysis. CONCLUSIONS: An approximate 3:1 ratio of animal-to-plant protein sources may represent an optimal balance for supporting nutrient intake and improving macronutrient profiles in the diets of Canadian children.
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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 | 0.006 |
| 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) | 0.003 | 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".