Translating knowledge into dietetic practice: a Functional Foods for Healthy Aging Toolkit
Bibliographic record
Abstract
The advance of functional foods has evolved because of research linking functional foods to health, a regulatory environment that allows health claims on foods, and consumer demand for health-promoting food products. Among consumers, the rapidly growing older adult segment is poised to benefit from functional foods because of age-related health issues that are linked to food and health. Registered Dietitians (RDs) are recognized as food and nutrition experts and are well positioned to communicate the benefits of functional foods. The Functional Foods for Healthy Aging Toolkit was developed to provide guidance and resource materials to assist RDs in communicating with older adults about functional foods. The toolkit provides background on functional foods, including definitions, regulations, and case studies of functional food product labels. The role of functional foods in Canada's aging demographic is examined and the relevance to disease risk is discussed. The toolkit is appended with educational resource sheets on common functional food bioactives, including antioxidants, dietary fibre, omega-3 fatty acids, plant sterols, prebiotics, and probiotics. This publicly available toolkit can help RDs and other healthcare professionals in their interactions with older adults to maximize the value and health benefits that dietary inclusion of functional foods can offer.
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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 | 0.041 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".