MétaCan
Menu
Back to cohort
Record W2810505450 · doi:10.1080/21551197.2018.1482811

Review of Nutrition Screening and Assessment Practices for Long-Term Care Residents

2018· article· en· W2810505450 on OpenAlexaffabout
Shanthi Johnson, Roseann Nasser, Kayla Rustad, Jennifer Chan, Christina Wist, Aisha Siddique, Heather Tulloch

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2018
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsRegina Qu'Appelle Health RegionUniversity of ReginaSaskatchewan Health
Fundersnot available
KeywordsMedicineMalnutritionLong-term careGerontologyPhonePopulationEnvironmental healthNursing

Abstract

fetched live from OpenAlex

The older adult population in Canada is growing, creating a greater demand for long-term care (LTC) facilities. Seniors living in LTC are more vulnerable to malnutrition, making it important to implement nutrition screening tools on a routine basis. The purpose of this study was to explore the practices of Registered Dietitians (RDs) related to nutritional screening, nutritional assessment, and follow-ups conducted within LTC facilities. This study also explored possible barriers hindering the application of these practices. Nine RDs from two health regions in Southern Saskatchewan completed a phone interview to address nutrition care practices/policies and barriers in LTC facilities. Results showed a considerable amount of variability in nutrition care practices for screening and assessment with lack of time identified as the greatest barrier. These findings highlight the importance of having consistent policies and a sufficient amount of RDs available in LTC facilities to provide the expected level of nutrition care for residents.

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: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.226
Threshold uncertainty score0.361

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 head0.095
GPT teacher head0.462
Teacher spread0.367 · 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
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

Citations6
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nutrition in Gerontology and GeriatricsSame topicNutrition and Health in AgingFrench-language works237,207