<i>Mealtimes as Active Processes</i>in Long-term Care Facilities
Bibliographic record
Abstract
Mealtimes are central to the nutritional care of residents in long-term care facilities. There has been little Canadian research to guide interdisciplinary practice around mealtimes. This study included a grounded theory approach to explore mealtime experiences of 20 people with dementia living in two long-term care facilities, and the meal-related care they received from registered nurses, health care aides, and dietitians. Theoretical sampling directed the collection and analysis of data from mealtime observations in special care units and key informant interviews with care providers. The constant comparison method was used to analyze and conceptualize the data. A substantive theory emerged with three key themes: 1. Each mealtime is a unique process embedded within a long-term care facility's environment. 2. Residents are central to the process through their actions (i.e., arriving, eating, waiting, socializing, leaving, and miscellaneous distracted activities). 3. Internal (i.e., residents' characteristics) and external (i.e., co-resident, direct caregiving, indirect caregiving, administrative, and government activities) influences affect residents' actions at mealtimes. The theory suggests that optimal mealtime experiences for residents require individualized care that reflects interdisciplinary, multi-level interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".