Insights Regarding Mealtime Assistance for Individuals in Long-term Care
Bibliographic record
Abstract
Focus groups were conducted with staff in a geriatric care facility who provided mealtime assistance during a quarantine that prevented family members from entering the facility. The volunteers' accounts reflected 3 primary themes that influenced their experience as mealtime assistants. First, the role of volunteer-recipient relationships emerged as paramount in facilitating optimal mealtime care. Strong reinforcement was derived from small signs of gratitude and awareness in the residents' nonverbal behaviors. This fostered the volunteers' sense of fiduciary responsibility toward the resident, thereby facilitating a meaningful and successful mealtime experience. Second, it was clear that the experience of being a mealtime assistant evolved over time, with changes in volunteer attitude mediated directly by the relationships that developed between volunteers and recipients. Lastly, the data reflect a strong awareness among volunteers of the challenges faced by nursing staff on a daily basis with respect to meeting the mealtime needs of residents in long-term care institutions, and a concern that nursing staff have insufficient time to develop intimate relationships with residents at the mealtime. These data strongly suggest that volunteer-assisted mealtime programs that focus primarily on social relationships can enhance the mealtime experience for residents in long-term care institutions.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".