Nutrient Content and Nutrient Function Health Claims in Relation to Bean Consumption in Older Adults
Bibliographic record
Abstract
Bean consumption increases diet quality and reduces age-related chronic disease risk yet consumption is low among North Americans. Research in older adults regarding the consumption of beans and related health claims may address this gap. The purpose of this study was to explore bean consumption among older adults (65+ years) and their response to health claims in relation to bean consumption. Community-dwelling older adults (n = 250; 76% female) completed a validated researcher-administered questionnaire to explore the prevalence of bean consumption, awareness and acknowledgment of nutrient content claims (NCCs) and nutrient function claims (NFCs), and the likelihood of these claims influencing bean consumption. The prevalence of bean consumption was 51.2%. Awareness of NCCs and NFCs was reported by 94.4% and 64.0% of participants, respectively; and of those aware, these health claims were acknowledged by 91.5% and 85.6% of participants, respectively. Those with a higher education level were significantly more likely to be aware of NCCs and NFCs and acknowledge NFCs. Those with no health conditions and those with a lower level of prescription medication use (≤2 per day) were significantly more likely to be aware of NFCs and NCCs, respectively. Of the nutrients mentioned in NCCs, those most frequently reported to increase bean consumption included dietary fibre (78.0%), iron (71.6%) and calcium (70.0%). For NFCs, the biological roles related to calcium, iron and vitamin B6 were the most frequently reported to increase bean consumption (72.0%, 67.6%, and 64.4%, respectively). This research will help advance dietary strategies that can contribute to healthy aging and promote Ontario agriculture. (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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".