Dining experience, foodservices and staffing are associated with quality of life in elderly nursing home residents
Bibliographic record
Abstract
PURPOSE: Few studies have quantitatively investigated potential relationships between quality of life (QOL) in long term care (LTC) and foodservices. OBJECTIVE: To investigate if dining experiences, and food and nutritional services affect elderly nursing home residents' QOL. DESIGN AND PARTICIPANTS: A total of 395 residents in 38 nursing homes participated in this cross-sectional study. MEASUREMENTS: Information on dining experiences and QOL was gathered by face-to-face interviews with cognitively intact residents; primary institutional caregivers completed a questionnaire for cognitively impaired residents. Additional data were also obtained from participants' medical charts and from administrators and foodservice managers. Multivariate ordinary least squares (OLS) regression was used to determine which institutional characteristics were related to QOL. RESULTS: Number of dining companions, autonomy in relation to food, tray meal delivery service, and ratio of residents per resident assistant were significantly related to QOL in both cognitively intact and cognitively impaired residents. For cognitively intact residents, number of medical conditions, therapeutic menus, and use of china dishes were also related to QOL. For cognitively impaired residents, independence with eating and frequency of menu revision were also related to QOL. CONCLUSION: Modifying certain aspects of food and nutritional services, as well as residents' dining experience, may improve QOL of elderly LTC 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".