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Record W23448231

Meals-on-wheels improves energy and nutrient intake in a frail free-living elderly population.

2007· article· en· W23448231 on OpenAlexaff
M-A Roy, Hélène Payette

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of Sherbrooke
Fundersnot available
KeywordsMedicineAnalysis of covarianceNutrientGerontologyPsychological interventionLife expectancyPopulationEnvironmental healthPhysical therapyMathematicsNursingStatistics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: With the increasing life expectancy and associated health care cost in the elderly population, it is fundamental to study and improve interventions that help older persons to have a better and healthier life in their home for a longer period. OBJECTIVES: Evaluate the effect of Meals-on-Wheels (MOW) on dietary intakes of frail elderly. DESIGN: An untreated control group quasi-experimental design with pretest and post-test was used to compare users (n = 20) and non-users (n = 31) of MOW. Descriptive and dietary data were compared at pretest and 8 weeks later. Analysis of Covariance (ANCOVA) was used to control for initial differences between groups. RESULTS: Both groups were similar at pretest except for weight (p = 0.028) and weekly number of meals eaten outside the home (p = 0.008). In both groups, dietary intakes at pretest were below Estimated Average Requirements (EAR) for the same nutrients. At post-test, intake of most nutrients increased in the Experimental group in comparison with the Control group. After controlling with the ANCOVA model, increases were significant for energy (p = 0.050), protein (p = 0.030), lipid (p = 0.034) and thiamin (p = 0.035). Provision of MOW did not permit to achieve a low risk of nutrient inadequacy in the Experimental group. CONCLUSIONS: MOW programs improve dietary intakes of recipients. However, a more intensive intervention is needed to prevent nutrient deficiencies in this group.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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 teacher head, 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

Citations67
Published2007
Admission routes1
Has abstractyes

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