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Record W2982927075 · doi:10.1177/0733464819885718

Factors That Influence the Reintegration to Normal Living for Older Adults 2 Years Post Hip Fracture

2019· article· en· W2982927075 on OpenAlexafffund
Katherine S. McGilton, Abeer Omar, Steven Stewart, Charlene H. Chu, Meagan B. Blodgett, Jennifer Bethell, Aileen M. Davis

Bibliographic record

VenueJournal of Applied Gerontology · 2019
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsTrent UniversityToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersInstitute of AgingCanadian Institutes of Health Research
KeywordsHip fractureActivities of daily livingGerontologyMedicineIndependent livingCaregiver burdenPsychological interventionPhysical therapyDementiaOsteoporosisPsychiatryDisease

Abstract

fetched live from OpenAlex

Objectives: This study aims to identify factors that influence older adults’ reintegration to normal living 2 years following a hip fracture and the association between caregiver burden and reintegration over time. Methods: This longitudinal cohort study followed 76 community-dwelling older adults and their caregivers for 2 years post-hip fracture. The primary outcome was Reintegration to Normal Living Index (RNLI), and the secondary outcome was caregiver burden. Results: Older adults scored significantly lower on RNLI at 18 to 24 months if they had few social interactions, cognitive impairment, or lower pre-fracture functional status. During follow-up, greater independence in activities of daily living and greater mobility were each positively associated with RNLI. Caregiver burden reduced if reintegration improved. Implications: Results suggest a need for targeted interventions for older adults’ post-hip fracture to improve their function to enhance their reintegration to normal living and to support caregivers in decreasing their burden of care.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.275
Teacher spread0.262 · 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
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

Citations6
Published2019
Admission routes2
Has abstractyes

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