<i>Use of Oral Nutrition Supplements</i> In Long-term Care Facilities
Bibliographic record
Abstract
PURPOSE: Practices related to oral nutrition supplement (ONS) use were examined in elderly people living in long-term care (LTC) facilities. METHODS: Thirteen LTC facilities within a large regional health authority participated, and 17 people responsible for prescribing ONS in their facilities were interviewed, using a key informant telephone survey. A survey on ONS practice was modified, pilot tested, and used. RESULTS: Oral nutrition supplements were primarily prescribed by nursing staff (59%), followed by physicians, registered dietitians, or other staff; ONS use was prescribed for decreased intake, unintentional weight loss, or wound healing. Various ONS products (e.g., Ensure, Boost, or Resource 2.0) were prescribed. Only 18% of respondents reported using alternative food options first to supplement nutritional intake, before introducing ONS. In terms of follow-up and evaluation, the measures of improvement included weight gain, wound healing, or improved well-being; reasons for discontinuation included weight gain, increased intake, or death. CONCLUSIONS: Within LTC settings, the prescription and monitoring of ONS vary considerably. Evidence-based guidelines for the prescription and monitoring of ONS and for the use of a food-first strategy should be developed, implemented, and evaluated to optimize the nutritional health of the elderly in LTC facilities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".