73 Nutritional impact of excluding red meat from the Canadian diet
Bibliographic record
Abstract
Abstract Foods of animal origin, including beef, pork, and lamb, provide macro- and micro-nutrients which may be difficult to obtain in adequate quantities from plant-based foods alone. Nonetheless, recent literature suggests that a shift away from red meat in favor of plant-based diets can reduce household greenhouse gas emissions without adversely affecting nutrient intake. The objective of the current study was to examine the nutrient intake of consumers who eliminated red meat from their diet, compared with those who did not. The impact of red meat elimination on nutrient intake was estimated using inverse probability weighting with regression adjustment estimator and bias-corrected matching estimators, using data garnered from the 2015 Canadian Community Health Survey. We also examined if self-selected diets without red meat fulfilled the Recommended Daily Allowance (RDA) of macronutrients, vitamins, and minerals, included in Canada’s Dietary Reference Intake Tables. Adequate Intake (AI) was used when RDA could not be determined. Consumers who eliminated red meat from their diet reported significantly lower intake of several nutrients, including total saturated fatty acids, saturated 18:0 octadecanoyloxy fatty acid, protein, riboflavin, niacin, vitamins D, B6 and B12, and zinc, as compared with those who did not. Eliminating red meat also resulted in lower dietary cholesterol and sodium intake. Further, consumers who eliminated red meat reported mean daily intake levels below the daily requirements of Calcium, Magnesium, Potassium, and Vitamins A, B6 and D. The results from this study suggest that elimination of red meat may lead to dietary substitutions that result in nutritional deficiencies for Canadian consumers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".