Consumption of Legume Dishes by Canadian Adults: Exploratory Analysis of the Canadian Community Health Survey (Nutrition) 2004 and 2015
Bibliographic record
Abstract
The Canadian diet is commonly known for its large portion sizes and caloric density. Social and environmental factors contribute to obesity in a variety of ways that are underappreciated and attributed to its rapid growth. This trend may be partly reversed by eating foods lower in energy density, high in fiber, and moderate in protein such as legumes, defined as the dried seeds of the Leguminosae plant family. Canadians have been encouraged to increase consumption of plant-based protein sources, like legumes, by the latest Canada’s Food Guide. For possible interventions, it is necessary to better understand legume consumers, when they consume legumes, and what types of legume dishes they consume. Legume consumption was investigated in the 24-hour diet recall data from two population-based surveys, the Canadian Community Health Surveys 2004 and 2015, focusing on socio-demographic characteristics of legume consumers; examining meal compositions; and assessing any changes between the two surveys. Considering the possibility of adapting and incorporating the recipes into the Canadian context, the meal behaviors of consumers from different ethnic backgrounds where legumes are part of a traditional diet were of particular interest. They were also compared based on their demographic variables, their consumption of legume foods, and their food preferences. Several descriptive techniques, as well as principal component and cluster analyses, were used to determine how legumes fit into the dietary patterns of the Canadian population. Where it was not possible to assess consumption of specific legume dishes, food group data were analyzed. The percentage of people who consumed legumes increased from 6.2 in 2004 to 10.4 in 2015, after adjustment for education, age, sex, cultural or racial origin, body mass index (BMI), and marital status. The proportional increase in the use of more varieties of legumes and increased intake of hummus were notable. As Canadians tend to consume legumes primarily at midday and at dinner, these analyses suggest that a useful strategy for increasing legume consumption may be to combine grains, pasta, and fruits and vegetables at midday and dinner. Adaptation of cultural foods from the major immigrant cuisines should also be explored.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".