ASSESSING THE MEALTIME ENVIRONMENT IN CANADIAN LONG-TERM CARE HOMES USING THE MEALTIME SCAN
Bibliographic record
Abstract
The mealtime environment in long term care (LTC) may influence food intake of residents, and could improve food intake. Making the Most of Mealtimes (M3) is a cross-sectional, multi-site study with data collected from 82 dining rooms in 32 LTC homes in 4 Canadian provinces. The Mealtime Scan (MTS) was developed to quantify the overall dining atmosphere and includes items that assess the physical and social environments and person-centred care practices. MTS was completed 4–6 times in each dining room and average values used for analysis. Protein and energy intake of residents (n=639) was collected with non-consecutive weighed 3-day records. Units were stratified based on whether or not they specialised in dementia care. Regression analyses were used to identify MTS items adjusted for age, gender and cognitive status that predicted individual energy and protein intake (p<0.05). In dementia care units, number of residents eating alone was positively associated with energy intake; while meal length, number of residents eating alone, and person-directed care practices were negatively associated with protein intake in designated dementia units. In general units, none of the mealtime environment characteristics, as measured by the MTS, were associated with energy intake; protein intake was positively associated with number of persons in the dining room and negatively associated with person-directed care practices towards residents that required eating assistance. This analysis suggests that environmental features of designated dementia and general units differ in their association with residents’ food intake. Strategies to support food intake should be tailored to the target population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".