Nutritional Status and Feeding Difficulty of Older People Residing in Nursing Homes: A Cross-Sectional Observational Study
Bibliographic record
Abstract
Aims: To investigate the nutritional status and feeding behaviours of nursing home residents and the impact of cognitive impairments and feeding difficulties on nutritional health. Design: A cross-sectional observational design was employed. Methods: The study assessed 51 nursing home residents using the Mini Nutritional Assessment Short-Form (MNA-SF) for nutritional status, the Feeding Difficulty Index (FDI) for mealtime behaviours, and the MoCA (Montreal Cognitive Assessment or The MoCA Test) for cognitive function. Results: The average age of participants was 87.8 years. Nearly half (47.1%) were at high risk of malnutrition, and 13.7% were classified as malnourished. The average MoCA score was 14, indicating moderate cognitive impairment, which was inversely associated with nutritional status. Feeding difficulties were common, as follows: 74.5% of residents paused feeding for over one minute, and 62.8% were distracted during meals. A longer duration of nursing home residency was associated with poorer nutritional outcomes. Overall, 65% of residents required mealtime assistance, with higher FDI scores correlating with greater support needs. Significant positive correlations were found between cognitive function and nutritional status (r = 0.401, p = 0.037) and between food intake and nutritional status (r = 0.392, p = 0.004). In contrast, residency duration (r = −0.292, p = 0.037) and feeding difficulties (r = −0.630, p < 0.001) were negatively associated with MNA-SF scores. FDI scores were strongly associated with the level of assistance required during meals (r = 0.763, p < 0.001). This study highlights the critical need for targeted nutritional assessments and interventions in nursing homes, especially for residents with dementia facing cognitive impairments and feeding difficulties. Enhancing staff training on recognising and addressing eating challenges and risk factors is essential for improving nutritional well-being. Conclusions: The study highlighted the profound impact of cognitive impairments and feeding difficulties on the nutritional health of nursing home residents, indicating a high prevalence of malnutrition and a need for comprehensive mealtime assistance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".