Nutrition Screening for Seniors in Health Care Facilities: A Survey of Health Professionals
Bibliographic record
Abstract
PURPOSE: Several studies show that malnutrition is prevalent in health care facilities, especially among elderly patients and nursing home residents. Although validated screening tools exist, little evidence exists on the feasibility of implementing nutrition screening in health care facilities. We examined New Brunswick health care professionals' perceptions of and practices involving nutrition screening in elderly clients, as well as barriers to screening. METHODS: A survey was conducted with questionnaires intended for physicians, nurses, and dietitians. RESULTS: Participants were 457 health care professionals (physicians, 34.6%; nurses, 50.3%; dietitians, 15.1%). Perceptions of nutrition screening varied. For example, most nurses (94.7%) and dietitians (98.5%) indicated that screening was important/very important, while only 63.5% of physicians indicated this. Screening methods also differed among professionals and few used a screening tool. Several barriers to implementing nutrition screening were reported, such as lack of time, lack of professional resources, and clients' short stays. CONCLUSIONS: These findings will help professionals address the feasibility of implementing standardized screening tools in health care facilities. A more consistent and systematic approach for detecting populations at high nutritional risk may result.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".