<i>Nursing Home Food Services</i>Linked with Risk of Malnutrition
Bibliographic record
Abstract
PURPOSE: Links between food service characteristics and residents' risk of malnutrition were examined. METHODS: Cognitively intact residents meeting inclusion criteria and living in one of 38 participating nursing homes were randomly sampled. The final sample consisted of 132 residents, who were screened for risk of malnutrition and completed a face-to-face interview questionnaire about dining experiences. Additional data came from participants' medical charts, and each institution's food service manager completed a written questionnaire. Frequencies and logistic regressions were used to describe the sample and to examine relationships between risk of malnutrition and food service characteristics. RESULTS: Overall, 37.4% of participants were at risk of malnutrition. Food service factors, including food packages, lids, and dishes that were difficult to manipulate (b=0.285, p=0.009), bulk food-delivery systems (b=1.329, p=0.036), overall food satisfaction (b=0.253, p=0.044), menu cycle length (b=-2.162, p=0.003), and porcelain dishes (b=-0.345, p=0.052), all were significantly associated with risk of malnutrition. CONCLUSIONS: Our findings clearly show a need for nursing homes to modify certain aspects of food service that may increase the risk of malnutrition among cognitively intact residents.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".