Return to the Moon: Site Selection Process and Considerations for NASA's Robotic Lunar Exploration Program (RLEP)
Bibliographic record
Abstract
Awareness and knowledge of nutrient-dense foods are important for older adults to help them make dietary choices that support a food-first approach to healthy aging. This is especially important since age is a major risk factor for chronic disease and the proportion of older adults in North America is increasing. Beans can contribute to a food-first approach to healthy aging as they are nutrient-dense and can reduce the risk of chronic diseases. However, studies exploring awareness and knowledge of beans in older adults are lacking. Therefore, the aim of this study was to explore older adults' awareness of beans in relation to their nutrient content and role in chronic disease risk. Community-dwelling older adults (≥65 years old) were recruited and completed a validated researcher-administered questionnaire (<i>n</i> = 250), which was followed by 10 focus groups (<i>n</i> = 49). Results showed that the majority of older adults considered beans as 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 a serving of beans is high in 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 and constipation. 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 low in nutrients relevant to heart health including total fat, saturated and <i>trans</i> fat as well as cholesterol. This research can help to inform healthcare professionals and public health agencies to create specific dietary strategies focusing on increasing older adults' awareness and knowledge of beans in relation to their nutrient profile and role in promoting health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".