The association of food service satisfaction and nutrition among residents in long term care: The making the most of mealtimes study (M3)
Bibliographic record
Abstract
Introduction: Residents’ food service satisfaction (FSS) in long term care (LTC) can contribute to malnutrition risk. Low FSS has been found to lead to weight loss, malnutrition and a spiral of negative health effects. The Making the Most of Mealtimes Study (M3) examined the determinants of food and fluid intake of 639 residents in 32 diverse LTC homes in Canada. Objectives: 1) To identify characteristics of residents who completed the food service satisfaction survey. 2) To examine food service satisfaction in LTC. 3) To identify nutritional status indicators that affect FSS in LTC. 4) To construct validate the FSS survey administered for the M3 study. Methods: Secondary data from the M3 study obtained from 329 residents examined the FSS score (21 questions with a score range of 21-63), Cognitive Performance Score, Patient Generated – Subjective Global Assessment, energy intake, protein intake, texture modification, thickened fluids and prescribed oral nutritional supplement. Descriptive statistics, bivariate analysis, and one-way ANOVA were conducted (p-value < 0.05). Results: The respondents were 86.3 ± 7.6 (SD) years of age, 64.4% female, 51.1% with mild/moderate cognitive impairment (CI) and 38.3% were malnourished. Participants were highly satisfied with cold foods being served cold and foods being easy to chew. They were least satisfied with being hungry at meal times, hot foods being served hot, taste, and appearance of food. Mean FSS score was 55.5 ± 6.6 (82%). Associations were found between lower FSS scores and a modified diet texture prescription [t (327=3.141, p=0.002], thickened fluid prescription [t (327=2.458, p=0.014] and malnutrition diagnosis [t (327=2.354, p=0.020]. The FSS score was associated with modified diet textures (F=11.6, p=0.001).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".