MAKING THE MOST OF MEALTIMES: WHO IS PRESCRIBED MODIFIED TEXTURE FOODS IN CANADIAN LONG-TERM CARE
Bibliographic record
Abstract
Research suggests modified texture foods (MTFs) are prevalent among older adults in long term care (LTC), but characteristics of residents prescribed MTFs are sparsely documented. Making the Most of Mealtimes (M3) is a cross-sectional multi-site study with data from 32 LTC homes in four Canadian provinces (AB, MB, NB, ON). This secondary data analysis applied standardized terminology to examine the current prevalence of prescribed MTF and resident characteristics associated with their prescription. Resident characteristics were collected from health records and standardized procedures. Homes used 67 different terms to describe MTFs. Diets were re-categorized using the International Dysphagia Diet Standardization Initiative (IDDSI) Framework (pureed, minced, moist and soft/bite sized textures). Bivariate analyses were performed. MTFs were prescribed to 47% (n=298) of the M3 sample (n=639) and significantly differed across provinces (p<0.0001). Resident characteristics significantly associated with MTFs included: longer length of admission; dysphagia and malnutrition risk; dementia diagnosis; fewer vitamins/mineral supplements; prescription of oral nutritional supplementation; lower body weight, body mass index, and calf circumference; greater need of physical assistance; poor oral health status; and more challenges with eating, activities of daily living and impaired cognition. The prevalence of prescribed MTFs was high and diverse across provinces in Canada and residents on MTFs were more vulnerable than residents on regular texture diets. These findings demonstrate the value of using standardized terminology to inform policy and help identify subgroups more likely to consume these diets. (Funded by Canadian Institutes of Health Research).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".