LONG-TERM CARE HOME, STAFF, AND DINING ROOM CHARACTERISTICS ASSOCIATED WITH RESIDENTS’ FOOD INTAKE
Bibliographic record
Abstract
The Making the Most of Mealtimes (M3) prevalence study recruited 639 residents from 32 homes in four Canadian provinces (AB, ON, NB, MB) to determine factors associated with food intake (based on three days of weighed and estimated food records). Mealtime interactions with staff assessed with the Mealtime Relational Care Checklist (3 meals/resident), and the length of meals and assistance received (9 meals/resident) were recorded. Dining environments were assessed for physical features using the Dining Environment Audit Protocol, and the Mealtime Scan was used to record mealtime experience and ambiance. Staff (minimum of 10) in each home completed the Person-Directed Care questionnaire and home managers completed a survey describing home features and food services. Residents’ mean energy and protein intakes were 1572 ± 412 kcal/day and 58.4 ± 18.0 g/day, respectively. Average mealtime length was 40.2 ± 13.0 minutes, and residents received more positive than negative staff interactions at mealtimes (ratio: 2.2 ± 1.3). Bivariate analysis showed a negative association (p<0.01) for energy intake with number of residents to staff ratio during mealtimes and time since last menu revision (13–18 months). Protein intake was positively associated (p<0.01) with dietitian time but negatively associated with the evening meal (supper) being the main meal of the day, having two food preparation systems (rethermalization and traditional) and a longer time (>18 months) since last menu revision. This is the first study to consider home, staff and dining room characteristics and resident food intake, providing new opportunities for interventions. (Funded by Canadian Institutes of Health Research)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".