Older Adults' Awareness of Beans in Relation to Their Nutrient Content and Role in Chronic Disease Risk
Bibliographic record
Abstract
Chronic disease risk increases with age and as the North American older adult population grows, reducing risk is priority. One strategy is a food first approach by encouraging the awareness and consumption of nutrient-dense, health promoting foods such as beans. However, research focused on identifying awareness gaps about beans and their healthful attributes is lacking. The purpose of this study was to explore older adults' awareness of beans in relation to their nutrient profile and role in disease risk. Community dwelling older adults (n = 250; 65+ years; 76% female) completed a researcher-administered validated questionnaire to explore bean consumption and awareness of beans related to their nutrient content and role in health and disease. The majority of older adults considered beans a healthy food and thought consuming them could improve their health (99.2% and 98.0%, respectively); however, only 51.2% were bean consumers. While the majority (83.6%) of older adults were aware that one serving of beans is considered a high source of dietary fibre, bean consumers were significantly more likely to think that consuming beans could improve health areas related to dietary fibre including body weight management, constipation and diabetes. Furthermore, most (84.8%) older adults thought consuming beans could improve heart health; however, bean consumers were significantly more likely to be aware that one serving of beans is considered a low source of nutrients relevant to heart health including total fat, saturated and trans fat, and cholesterol. These data highlight gaps in older adults' awareness of beans and health and can contribute to the development of strategies to increase awareness and consumption. (Supported by the OMAFRA-University of Guelph partnership).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".