Increasing Plant-Based Meat Alternatives and Decreasing Red and Processed Meat in the Diet Differentially Affect the Diet Quality and Nutrient Intakes of Canadians
Bibliographic record
Abstract
Current evidence suggests a link between red and processed meat consumption and the risk of various cancers and other health outcomes. Using national survey data from the Canadian Community Health Survey (CCHS)-Nutrition 2015, we aimed to model a dietary scenario to assess the potential effects of increasing the intake of currently consumed plant-based meat alternatives by 100% and decreasing the consumption of red and processed meat by 50% on the diet quality and nutrient intakes of Canadians (≥1 year). This dietary scenario had no significant impact on dietary energy intake (p > 0.05), but resulted in a significant increase in the dietary intakes of fibre, polyunsaturated fatty acids, magnesium, and dietary folate equivalents (p < 0.05). On the other hand, this dietary scenario was accompanied by a significant decrease in protein (from 77.8 ± 0.6 g to 73.4 ± 0.6 g), cholesterol, zinc, and vitamin B12 intake (p < 0.05). Further, based on Nutrient Rich Food (NRF) scores, the overall nutritional value of the simulated diet was higher than the baseline diet. Our modeling showed that the partial replacement of red and processed meat with plant-based alternatives improves overall diet quality but may adversely affect the intake of some micronutrients, especially zinc and vitamin B12.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".