NUTRIENT INTAKE OF FEMALE RESIDENTS CONSUMING A PUREED DIET IN CANADIAN LONG-TERM CARE (LTC) HOMES
Bibliographic record
Abstract
Older adults residing in long-term care (LTC) face increased risk of malnutrition because of many factors, including decreased appetite, difficulty eating, and cognitive impairment. Issues associated with pureed texture diets (e.g., dysphagia, poor dentition, lack of appeal, and extra menu planning) further contribute to this risk. The current study examined the adequacy of nutrient intake among female LTC residents prescribed a pureed texture diet. Making the Most of Mealtimes (M3) is a cross-sectional study of 639 residents from 32 LTC facilities across four Canadian provinces (AB, MB, NB, ON). Of these, 67 residents (10.5%) were prescribed a pureed texture diet, 51 of which were female (88 ± 8 years old). Weighed food intake was measured on three non-consecutive days and analyzed using ESHA Food Processor software. Intake of energy and 25 nutrients were adjusted for intra-individual variability and compared to their corresponding Estimated Average Requirement (EAR) or Adequate Intake (AI) value. Mean energy intake among female consumers of pureed diets was 1487 ± 376 kcal/day. Estimated inadequacies were found for vitamins D, E and folate (>95% of individuals below EAR); and vitamin B6, calcium and magnesium (>50% but <90% below EAR). For nutrients with an AI, median intakes of dietary fibre, potassium and vitamin K were below their AI. These findings indicate that female residents prescribed a pureed texture diet in Canadian LTC homes have low intake of several micronutrients. Careful menu planning and nutrient-dense options for pureed texture diets in LTC are recommended. (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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".