Intake patterns and dietary associations of soya protein consumption in adults and children in the Canadian Community Health Survey, Cycle 2.2
Bibliographic record
Abstract
Soya foods are one of the recommended alternatives to meat in many dietary guidelines. While this is expected to increase the intake of some nutrients, potential concerns regarding others have been raised. The purpose of the present study was to examine the prevalence and the association of soya food consumption with nutrient intakes and dietary patterns of Canadians (age ≥ 2 years). Cross-sectional data from the 2004 Canadian Community Health Survey (Cycle 2.2; n 33,218) were used to classify soya consumers and non-consumers. Soya consumers were further divided into two groups based on their soya protein intake. Sample weights were applied and logistic regression analysis was used to explore the association between nutrient intakes and soya consumption, with cultural background, sex, age and economic status being included as covariates. On any given day, 3.3% (n 1085) of Canadians consume soya foods, with females, Asian Canadians and adults with post-secondary education being more likely to be soya consumers. As a whole, adolescent and adult respondents who had consumed at least one soya food during their 24 h dietary recall had higher energy intakes, as well as increased intakes of nutrients such as protein, fibre, vitamin C, vitamin B6, naturally occurring folate, thiamin, Ca, P, Mg, PUFA, Fe and K and lowered intakes of saturated fat. These data indicate that soya food consumption is associated with improved diet quality of Canadians. However, future research is necessary to investigate the association between increased energy intake and soya consumption.
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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 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".