MAKING THE MOST OF MEALTIMES: MALNUTRITION AND MODIFIED TEXTURE FOOD IN CANADIAN LONG-TERM CARE
Bibliographic record
Abstract
Modified texture foods (MTFs) are associated with a high prevalence of malnutrition (40–80%) among older adults in LTC, yet research to demonstrate the independent effect of MTFs is lacking. Making the Most of Mealtimes (M3) is a cross-sectional multi-site study that collected data in 32 LTC homes in four Canadian provinces (AB, MB, NB, ON). This secondary data analysis examined if prescription of MTFs as compared to a regular texture diet was associated with the risk of malnutrition in residents of LTC homes when diverse relevant covariates were considered. The Mini Nutritional Assessment Short-Form (MNA-SF) score was used to determine malnutrition. Use of MTFs, and resident and site characteristics were identified from health records, observations, and standardized assessments. Hierarchical linear regression analysis, accounting for clustering, was performed. A minced diet (F(1, 382)=5.01, p=0.03), as well as a pureed diet (F(1, 279)=4.95, p=0.03), were both significantly associated with risk of malnutrition among residents. After adjusting for age and gender, other significant covariates were: use of oral nutritional supplementation, cognitive impairment, eating challenges, and poor oral health. Given the significant association between consumption of MTFs and risk of malnutrition, MTFs need further consideration in regard to improving nutrient density and sensory appeal. These improvements could support food intake and quality of life and thus prevent malnutrition and other negative outcomes (e.g., depression, hospitalization). (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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".