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Record W2236087379 · doi:10.1111/nyas.12997

Improving food intake in persons living with dementia

2016· review· en· W2236087379 on OpenAlexaff
Heather Keller

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2016
Typereview
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
Fundersnot available
KeywordsDementiaGerontologyMalnutritionPsychosocialPsychological interventionIntervention (counseling)Quality of life (healthcare)MedicinePsychologyEnvironmental healthPsychiatryNursingDisease

Abstract

fetched live from OpenAlex

Persons living with dementia have many health concerns, including poor nutritional states. This narrative review provides an overview of the literature on nutritional status in persons diagnosed with a dementing illness or condition. Poor food intake is a primary mechanism for malnutrition, and there are many reasons why poor food intake occurs, especially in the middle and later stages of the dementing illness. Research suggests a variety of interventions to improve food intake, and thus nutritional status and quality of life, in persons with dementia. For family care partners, education programs have been the focus, while a range of intervention activities have been the focus in residential care, from tableware changes to retraining of self-feeding. It is likely that complex interventions are required to more fully address the issue of poor food intake, and future research needs to focus on diverse components. Specifically, modifying the psychosocial aspects of mealtimes is proposed as a means of improving food intake and quality of life and, to date, is a neglected area of intervention development and research.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.147
GPT teacher head0.375
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations40
Published2016
Admission routes1
Has abstractyes

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