Mealtimes in Nursing Homes: Striving for Person-Centered Care
Bibliographic record
Abstract
Malnutrition is a common and serious problem in nursing homes. Dietary strategies need to be augmented by person-centered mealtime care practices to address this complex issue. This review will focus on literature from the past two decades on mealtime experiences and feeding assistance in nursing homes. The purpose is to examine how mealtime care practices can be made more person-centered. It will first look at several issues that appear to underlie quality of care at mealtimes. Then four themes or elements related to person-centered care principles that emerge within the mealtime literature will be considered: providing choices and preferences, supporting independence, showing respect, and promoting social interactions. A few examples of multifaceted mealtime interventions that illustrate person-centered approaches will be described. Finally, ways to support nursing home staff to provide person-centered mealtime care will be discussed. Education and training interventions for direct care workers should be developed and evaluated to improve implementation of person-centered mealtime care practices. Appropriate staffing levels and supervision are also needed to support staff, and this may require creative solutions in the face of current constraints in health care.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".