Making the Most of Mealtimes (M3): protocol of a multi-centre cross-sectional study of food intake and its determinants in older adults living in long term care homes
Bibliographic record
Abstract
BACKGROUND: Older adults living in long term care (LTC) homes are nutritionally vulnerable, often consuming insufficient energy, macro- and micronutrients to sustain their health and function. Multiple factors are proposed to influence food intake, yet our understanding of these diverse factors and their interactions are limited. The purpose of this paper is to fully describe the protocol used to examine determinants of food and fluid intake among older adults participating in the Making the Most of Mealtimes (M3) study. METHODS: A conceptual framework that considers multi-level influences on mealtime experience, meal quality and meal access was used to design this multi-site cross-sectional study. Data were collected from 639 participants residing in 32 LTC homes in four Canadian provinces by trained researchers. Food intake was assessed with three-days of weighed food intake (main plate items), as well as estimations of side dishes, beverages and snacks and compared to the Dietary Reference Intake. Resident-level measures included: nutritional status, nutritional risk; disease conditions, medication, and diet prescriptions; oral health exam, signs of swallowing difficulty and olfactory ability; observed eating behaviours, type and number of staff assisting with eating; and food and foodservice satisfaction. Function, cognition, depression and pain were assessed using interRAI LTCF with selected items completed by researchers with care staff. Care staff completed a standardized person-directed care questionnaire. Researchers assessed dining rooms for physical and psychosocial aspects that could influence food intake. Management from each site completed a questionnaire that described the home, menu development, food production, out-sourcing of food, staffing levels, and staff training. Hierarchical regression models, accounting for clustering within province, home and dining room will be used to determine factors independently associated with energy and protein intake, as proxies for intake. Proportions of residents at risk of inadequate diets will also be determined. DISCUSSION: This rigorous and comprehensive data collection in a large and diverse sample will provide, for the first time, the opportunity to consider important modifiable factors associated with poor food intake of residents in LTC. Identification of factors that are independently associated with food intake will help to develop effective interventions that support food intake. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02800291 , retrospectively registered June 7, 2016.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".