Are Therapeutic Diets in Long-Term Care Affecting Resident Food Intake and Meeting their Nutritional Goals?
Bibliographic record
Abstract
Purpose: To examine health characteristics of long-term care (LTC) residents prescribed therapeutic diets (promoting or restricting intake of key food components), to determine how these diets influenced intake and whether there were differences in food intake and malnutrition risk between residents with and without restrictive diets. Methods: Secondary analysis of the Making the Most of Mealtimes Study includes 435 residents with no/mild cognitive impairment in 32 LTC homes across 4 provinces. Health records were reviewed for diet prescriptions and other characteristics. Weighed and observed food and fluid consumption over 3 nonconsecutive days determined intake. Bivariate and multivariable linear regressions identified associations between therapeutic diets and intake and key nutrients. Results: Almost half (42%) of participants were prescribed a therapeutic diet. Residents receiving restrictive diets (28%) consumed absolute calories consistent with those receiving a regular diet, but kcal/kg was significantly lower (22.1 ± 5.5 vs 23.6 ± 5.3). Low sodium and weight-promoting diets were the only therapeutic diets associated with their corresponding key nutrient profiles. Restrictive therapeutic diets were not associated with energy or protein intake when adjusting for covariates. Conclusions: Restrictive therapeutic diets among those with mild to no cognitive deficits do not appear to impair food intake.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".