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Record W3110601225 · doi:10.1177/2333721420975321

Characteristics of Socially Isolated Residents in Long-Term Care: A Retrospective Cohort Study

2020· article· en· W3110601225 on OpenAlexaffabout
Stephanie Chamberlain, Wendy Duggleby, Pamela B. Teaster, Carole A. Estabrooks

Bibliographic record

VenueGerontology and Geriatric Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLogistic regressionCohortRetrospective cohort studyDepression (economics)Long-term careMedicineMinimum Data SetGerontologyFamily medicineCohort studyPsychiatryNursingNursing homesInternal medicine

Abstract

fetched live from OpenAlex

Objectives: To identify socially isolated long-term care residents and to compare their demographic characteristics, functional status, and health conditions to residents who are not isolated. Methods: We conducted a retrospective cohort study using the Resident Assessment Instrument, Minimum Data Set, 2.0 (RAI-MDS) data, from residents in 34 long-term care homes in Alberta, Canada (2008–2018). Using logistic regression, we compared the characteristics, conditions, and functional status of residents who were socially isolated (no contact with family/friends) and non-socially isolated residents. Results: Socially isolated residents were male, younger, and had a longer length of stay in the home, than non-socially isolated residents. Socially isolated residents lacked social engagement and exhibited signs of depression. Discussion: Socially isolated residents had unique care concerns, including psychiatric disorders, and co-morbid conditions. Our approach, using a single item in an existing data source, has the potential to assist clinicians in screening for socially isolated long-term care 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 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)0.001
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 score0.137
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.375
Teacher spread0.343 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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