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Record W2001295418 · doi:10.1177/1533317507312805

Nutrition Education Needs and Resources for Dementia Care in the Community

2008· article· en· W2001295418 on OpenAlexaffabout
Heather Keller, Dana R. Smith, Cara Kasdorf, Sherry L. Dupuis, Lori Schindel Martin, Gayle Edward, Carly N. Cook, M. Rebecca Genoe

Bibliographic record

VenueAmerican Journal of Alzheimer s Disease & Other Dementias® · 2008
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of WaterlooToronto Metropolitan UniversitySt Joseph's Health CentreUniversity of Guelph
Fundersnot available
KeywordsDementiaGerontologyNutrition EducationMalnutritionForgettingMedicineWeight lossPsychologyNursingDiseaseObesity

Abstract

fetched live from OpenAlex

Nutrition problems and specificly weight loss are common in older adults with dementia living in the community. Study 1 involved interviews with 14 formal providers to identify the range of nutrition concerns they had experienced. In study 2, 74 Canadian Alzheimer Society chapters were surveyed by e-mail (23% participation rate) to determine nutrition concerns and education resources provided to clients. In all, 26 of these nutrition pamphlets or handouts were rated on content and format by 2 independent researchers using a standardized rating system. Common nutrition concerns identified in older adults with dementia living in the community include safety, weight loss, forgetting or refusing to eat, appetite, dysphagia, and unfavorable eating behaviors. Most resources provided to clients were considered low quality and did not match the nutrition concerns expressed by formal providers. Currently, there is a considerable knowledge translation gap around nutrition and dementia, and this study provides a basis for the future development of nutrition education resources.

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)0.006
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 score0.158
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.337
Teacher spread0.299 · 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

Citations38
Published2008
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Alzheimer s Disease & Other Dementias®Same topicNutrition and Health in AgingFrench-language works237,207