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Record W2141695757 · doi:10.1093/gerona/59.1.m68

Nutritional Risk Predicts Quality of Life in Elderly Community-Living Canadians

2004· article· en· W2141695757 on OpenAlexaff
Heather Keller, Truls Østbye, R. Goy

Bibliographic record

VenueThe Journals of Gerontology Series A · 2004
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGerontologyEnvironmental healthQuality of life (healthcare)Quality (philosophy)PsychologyRisk analysis (engineering)MedicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Although nutrition parameters have been linked to quality of life (QOL), few studies have determined if nutritional risk predicts changes in QOL over time in older adults. METHODS: 367 frail older adults were recruited from 23 service agencies in the community. Baseline interview included nutritional risk as measured by SCREEN (Seniors in the Community: Risk Evaluation for Eating and Nutrition), as well as a wide variety of covariates. Participants were contacted every 3 months for 18 months to determine QOL as measured by three questions from the Behavioral Risk Factor Surveillance System (BRFSS), a general whole-life satisfaction question, and a general change in QOL question. "Good physical health days" from the BRFSS was the focus of bivariate and multivariate analyses, adjusting for influential covariates. RESULTS: Seniors with high nutritional risk had fewer good physical health days and whole-life satisfaction at each follow-up point compared with those at low risk. In general, participants reported decreases in general QOL from baseline, with those in the moderate nutritional risk category most likely to report this change. Nutritional risk predicted change in good physical health days over time. Other important covariates include: gender, number of health conditions, perceived health, and age. CONCLUSIONS: Nutritional risk is an independent predictor of change in health-related QOL. The results also indicate a relationship between nutrition and the more holistic view of QOL. Evaluation studies of interventions for older adults need to include QOL measures as potential outcomes to further demonstrate the benefits of good nutrition.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.121
GPT teacher head0.386
Teacher spread0.265 · 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

Citations160
Published2004
Admission routes1
Has abstractyes

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