PERSON-CENTERED CARE (PCC) PRACTICES AND EATING ASSISTANCE IN CANADIAN LONG-TERM CARE FACILITIES
Bibliographic record
Abstract
Introduction: PCC can improve the mealtime experience for residents, yet practices specific to mealtimes are poorly understood. The Relational/Person-Centered Care in Dining checklist (RPCC) is a face valid and reliable instrument for assessing these interactions. Objectives: It was hypothesized that residents who require eating assistance would receive fewer positive mealtime interactions and care practices than those who do not require assistance. Methods: M3 is a cross-sectional study based in 32 long-term care homes across four Canadian provinces. Mealtime practices were observed by one of eight trained assessors for 637 randomly selected residents at three meals on non-consecutive days. Observation ratings were averaged across the three meals. An Edinburgh Feeding Evaluation in Dementia Questionnaire item determined if assistance was required (‘never/rarely’, ‘sometimes’, ‘often’). A summary score from RPCC was calculated based on the ratio of positive to negative mealtime specific interactions, with higher scores indicating more positive interactions. ANOVA determined if frequency of physical eating assistance a resident received was associated with the ratio of positive-to-negative RPCC interactions. Results: Almost one-quarter (23%) of residents required some level of assistance (11% sometimes; 12% often). Frequency of eating assistance was negatively associated with the ratio of positive to negative mealtime interactions [F(2, 632)=34.72, p=< 0.001; never/rarely=2.3, sometimes= 1.6, often=1.5]. Conclusions: Residents requiring more physical eating assistance received fewer positive interactions with staff compared to those requiring no assistance. Further work will examine the independence of this association and if it influences food intake. (Funding from Canadian Institutes of Health Research)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".