Cultural Perspectives in Feeding Difficulty in Taiwanese Elderly With Dementia
Bibliographic record
Abstract
OBJECTIVE: To investigate factors related to feeding difficulty that is shown in the interaction between nursing assistants and elderly residents with dementia. METHODS: Forty-eight residents and 31 nursing assistants were observed during meal times in a congregate dining room of a Taiwanese nursing home specializing in dementia care. Residents' eating behaviors, the dining environment, interactions between the nursing assistant and the residents, and feeding strategies used by nursing assistants were observed. Nutritional data for residents were collected from medical charts. The EdFED Scale #2 and interviews of nursing assistants provided information about feeding difficulty. Interviews also provided data on strategies used to address feeding difficulties. FINDINGS: The most frequent feeding difficulty was refusal to eat (37.5%). The strategies used by nursing assistants were limited. Nursing assistants stated they needed more training to address feeding difficulty in residents with dementia. CONCLUSIONS: Future research should be focused on the interface between the residents and nursing assistants who must identify various feeding difficulties and select appropriate interventions. CLINICAL RELEVANCE: Results might provide information that can be used to develop effective interventions and promote high-quality mealtime care in patients with dementia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".