The <scp>Food4Years</scp> Ageing Network: Improving foods and diets as a strategy for supporting quality of life, independence and healthspan in older adults
Bibliographic record
Abstract
By 2050, it is predicted that one in four people in the United Kingdom will be aged 65 years and over. Increases in lifespan are not always translated into years spent in good health. Incidence rates for chronic diseases are increasing, with treatments allowing people to live longer with their disease. There is good evidence to support changes to lifestyle to maintain or improve body composition, cognitive health, musculoskeletal health, immune function and vascular health in older adults. Much research has been done in this area, which has produced significant support for foods and nutrients that contribute to improved healthspan. Yet two major barriers remain: firstly, older adult consumers are not meeting current UK recommendations for macro- and micronutrients that could benefit health and quality of life and secondly, the UK-specific recommendations may not be sufficient to support the ageing population, particularly for nutrients with key physiological roles. More work is needed to improve intakes of specific foods, diets and nutrients by older adults, through a variety of mechanisms including (i) development of specific food products; (ii) improved clarity of information and (iii) appropriate marketing, and policy changes to enable incentives. The Food4Years Ageing Network aims to build a wide-reaching and multidisciplinary community that is committed to the development, integration and communication of healthy, affordable foods and specific diets for all older adults across the UK food landscape. The Network will identify evidence-based strategies for improving food intake and nutrition in older adults, paving the way to "living well while living longer."
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.042 |
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".