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Record W1720860478 · doi:10.1080/21551197.2014.960339

Interventions for Improving Mealtime Experiences in Long-Term Care

2014· review· en· W1720860478 on OpenAlexaff
Vanessa Vucea, Heather Keller, Kate Ducak

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2014
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsRegional Municipality of WaterlooResearch Institute for AgingUniversity of Waterloo
Fundersnot available
KeywordsPsychological interventionMedicineIntervention (counseling)Randomized controlled trialGerontologyLong-term careMEDLINENursingSurgery

Abstract

fetched live from OpenAlex

Poor food intake in residents living in long-term care (LTC) homes is a common problem. The mealtime experience is known to be important in the multifactorial causes of food intake. Diverse interventions have been developed, implemented, and/or evaluated to improve the mealtime experience in LTC; it is possible that multicomponent interventions will have a greater benefit than single activities. To identify the range of feasible and potentially useful interventions for including in a multicomponent intervention, this scoping review identified and summarized 58 studies that described and/or evaluated mealtime experience interventions. There were several randomized controlled trials, although most studies used less rigorous methods. Interventions that are multicomponent (e.g., food service, dining environment, staff education) and target multilevel factors (e.g., residents, staff) in LTC appear to be feasible, with a variety of outcomes measured. Further research is still needed with more rigorously designed studies, confirming effectiveness, feasible implementation, and scaling up of efficacious interventions.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.631
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.108
GPT teacher head0.449
Teacher spread0.341 · 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 designSystematic review
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

Citations52
Published2014
Admission routes1
Has abstractyes

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