FOOD SERVICES IN LONG-TERM CARE HOMES ARE ASSOCIATED WITH RESIDENTS’ FOOD INTAKE
Bibliographic record
Abstract
Background: Food and nutrient intake of Canadian long-term care residents is commonly inadequate. Factors influencing resident food intake and nutritional quality of menus are numerous and those related to food service are often poorly investigated. Objective: To: a) describe food service characteristics of diverse Canadian long-term care homes, b) determine if there are provincial differences in these characteristics, c) determine the association between these characteristics and food intake of residents, and d) determine the association between these characteristics and the nutritional quality of the menus. Design: A cross-sectional study. Setting: Thirty-two long-term care homes in four Canadian provinces (Alberta, Manitoba, New Brunswick and Ontario). Participants: 639 residents. Measurements: Food service operations were reported by home administrators using a standardized questionnaire and summarized by province for analysis. Resident food intake was assessed by weighing and observing intake over three non-consecutive days including one weekend day. Absolute mean energy and protein intake per resident was used in analyses, as well as the nutritional quality of the diet (iMAR). Menus (regular and pureed) were analyzed for nutrient content and compared to the Dietary Reference Intakes to determine the menu Mean Adequacy Ratio (mMAR), a score that summarizes the nutrient quality. Results: Daily energy and protein intake as well as iMAR were not significantly different across provinces. The timing of the biggest meal of the day, the proportion of commercially prepared food and the mMAR score were the only food service variables significantly different between provinces. Homes who reviewed their menus in the past six months (72%), was positively associated with participant's energy (=23.0; p=0.02) and protein intake (=3.1; p=0.03). Food production system was associated with pureed menu mMAR score (F=5.46; p=0.01). Supper as the biggest meal of the day (67.7 %), was negatively associated with participants' protein intake (=-16.73; p=0.02) while the pureed menu mMAR score was positively associated with the iMAR score (=0.61; p=0.01). Conclusion: To promote sufficient intake, effort is needed to ensure menus meet dietary requirements. Most homes provided the largest meal of the day in the evening and this practice appears to lead to lower protein intake. Relatively few food service variables analysed were associated with resident intake, suggesting that others factors (i.e., resident attributes, mealtime environment, etc.) should also be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".