Addressing Nutrition Risk in Older Adults in Community Settings
Bibliographic record
Abstract
Nutrition risk, and subsequent malnutrition, can be common in community- dwelling older adults in Europe and other high-income regions. While a major factor in predicting loss of independence, hospitalization and mortality, they also largely preventable, but older adults often have limited supports to prevent or manage nutrition concerns in communities, compared with hospital settings. Primary care and other community settings are well-situated to provide screening and follow-up, although several barriers may exist when implementing preventative or curative interventions. Indeed, malnutrition prevalence in older adults ranges from less than 1% to almost 20% in high-income countries, which suggests there is disparity in prevention and treatment strategies. This narrative review aims to highlight the prevalence and costs of malnutrition, then focus on recent examples of community-based nutrition risk mitigation to guide the establishment of care pathways for malnutrition management in primary and community care. In particular, care pathways incorporating routine screening, which includes monitoring of dietary intake and weight of patients, with risk-based follow-up are shown to reduce nutrition risk. Likewise, leveraging both medical interventions from dietitians and non- medical interventions such as addressing food insecurity or social isolation are required to mitigate nutrition risk.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".