<i>Defining Malnutrition Risk</i>For Older Home Care Clients
Bibliographic record
Abstract
PURPOSE: The Resident Assessment Instrument-Home Care (RAI-HC) is widely used to assess needs of home care clients and includes five items used to screen for malnutrition. This study involved defining malnutrition risk and identifying other items within the RAI-HC that might improve malnutrition screening among adults aged 65 or older receiving home care. METHODS: A literature review, three focus groups of community care access centre case managers (n=29), and five key informant interviews with registered dietitians were used to identify malnutrition risk factors and indicators. A nominal group (n=5) was used to rank RAI-HC malnutrition risk items. Data were charted and integrated to create the final list of potential risk factors. RESULTS: Seven malnutrition indicators (dietary intake, appetite, dysphagia, nutrition support, end-stage disease, weight status, and fluid intake) and seven risk factors (health status, functional ability, self-reported poor health, mood status, social function, cognitive performance, and trade-offs) were considered important concepts in the construct of malnutrition for older home care clients. CONCLUSIONS: These items identified through divergent methods form the basis for developing a screening-for-malnutrition-risk tool for home care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".