Soy consumption fits within a healthy lifestyle
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe health‐related factors and behaviors associated with soy consumption and to present a better identification of a soy consumer's profile at meso‐level. Design/methodology/approach A total of 104 women and 49 men, 22‐77 years old and employed at the Vrije Universiteit Brussel, participated in the study. A physical activity questionnaire and a food frequency questionnaire were administered. Body height, weight, waist and hip circumferences, body fat percentage (2‐pole BIA), blood glucose and total blood cholesterol were measured in a fasting state. Findings Compared with the general population, the sample showed healthier eating habits (breakfast frequency, fruit and vegetable consumption) and lower prevalence of smoking. Women regularly consuming soy had lower waist circumference, body fat percentage and total cholesterol levels than infrequent soy consumers. Men consuming soy foods regularly participated significantly more in high‐intensity physical activities and consumed less meat, poultry and fish. Research limitations/implications Owing to recruitment in a university setting, a healthy volunteer effect and socio‐economic bias may exist. Therefore, generalization of the results is not possible and interpretation of the results must be performed with the utmost caution. Practical implications It can be concluded that, especially in men, soy consumption fits in a healthy lifestyle. Originality/value Regular soy‐consuming women differed from infrequent soy‐consuming women on anthropometrics and cholesterol, while regular consuming men differed behaviorally from infrequent soy‐consuming men. Overall, regular soy consumers were generally more health‐conscious and had better health outcomes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".