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Record W2766085697 · doi:10.1186/s40795-017-0198-3

Nutritional quality of regular and pureed menus in Canadian long term care homes: an analysis of the Making the Most of Mealtimes (M3) project

2017· article· en· W2766085697 on OpenAlexafffundabout
Vanessa Vucea, Heather Keller, Jill Morrison, Alison M. Duncan, Lisa M. Duizer, Natalie Carrier, Christina Lengyel, Susan E. Slaughter

Bibliographic record

VenueBMC Nutrition · 2017
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of AlbertaUniversity of ManitobaUniversité de MonctonUniversity of GuelphResearch Institute for AgingUniversity of Waterloo
FundersInstitute of Nutrition, Metabolism and DiabetesCanadian Institutes of Health Research
KeywordsClinical nutritionMedicineGerontologyTerm (time)Long-term careQuality (philosophy)Public healthEnvironmental healthNursingInternal medicine

Abstract

fetched live from OpenAlex

Long term care (LTC) menus need to contain sufficient nutrients for health and pureed menus may have lower nutritional quality than regular texture menus due to processes (e.g., recipe alterations) required to modify textures. The aims of this study were to: determine adequacy of planned menus when compared to the Dietary Reference Intake (DRI); compare the energy, macronutrients, micronutrients and fibre of pureed texture and regular texture menus across LTC homes to determine any texture, home or regional level differences; and identify home characteristics associated with energy and protein differences in pureed and regular menus. Making the Most of Mealtimes (M3) is a cross-sectional multi-site study that collected data from 32 LTC homes in four Canadian provinces. This secondary analysis focused on nutrient analysis of pureed and regular texture menus for the first week of the menu cycle. A site survey captured characteristics and services of each facility, and key aspects of menu planning and food production. Bivariate analyses were used to compare menus, within a home and among and within provinces, as well as to determine if home characteristics were associated with energy and protein provision for both menus. Each menu was qualitatively compared to the DRI standards for individuals 70+ years to determine nutritional quality. There were significant provincial and menu texture interactions for energy, protein, carbohydrates, fibre, and 11 of 22 micronutrients analyzed (p < 0.01). Alberta and New Brunswick had lower nutrient contents for both menu textures as compared to Manitoba and Ontario. Within each province some homes had significantly lower nutrient content for pureed menus (p < 0.01), while others did not. Fibre and nine micronutrients were below DRI recommendations for both menu textures within all four provinces; variation in nutritional quality existed among homes within each province. Several home characteristics (e.g., for-profit status) were significantly associated with higher energy and protein content of menus (p < 0.01). There was variability in nutritional quality of menus from LTC homes in the M3 sample. Pureed menus tended to contain lower amounts of nutrients than regular texture menus and both menus did not meet DRI recommendations for select nutrients. This study demonstrates the need for improved menu planning protocols to ensure planned diets meet nutrient requirements regardless of texture. ClinicalTrials.gov ID: NCT02800291, retrospectively registered June 7, 2016.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.084
GPT teacher head0.410
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2017
Admission routes3
Has abstractyes

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