Trends in Plant-Based Diets and the Associated Health Characteristics among Canadians
Bibliographic record
Abstract
In Canada, unhealthy dietary patterns comprise diets with poor nutrient density and are associated with chronic conditions. Plant-based diets have gained popularity due to their ability to provide a nutritionally adequate healthy diet. This study aims to compare sociodemographic, socioeconomic, and health characteristics, and diet quality between Canadian adults following plant-based and omnivore diets as well as assess the extent to which key nutrient intakes are of public health concern among Canadians following plant-based diets. The study used nationally representative nutritional data from the 2015 Canadian Community Health Survey and descriptive statistics were computed. The analysis determined that Canadians following strict plant-based diets (1% of total population) were significantly more likely to be an immigrant to Canada, less likely to meet national physical activity guidelines, and less likely to be overweight, compared to Canadians following omnivore diets. Compared to omnivore diets, plant-based diets were nutritionally superior according to the Nutrient-Rich Food index. Continued knowledge translation on what comprises healthy plant-based diets, public guidance on the intersection between diet and health, and the completion of prospective cohort studies are needed. To conclude, the research suggests well-planned plant-based diets, in comparison to omnivore diets, offer a nutrient-dense diet.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".