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Record W2945819603 · doi:10.1080/09638288.2019.1595181

Patient and caregiver experiences on care transitions for adults with a hip fracture: a scoping review

2019· review· en· W2945819603 on OpenAlexafffund
Maliha Asif, Lauren Cadel, Kerry Kuluski, Amanda C. Everall, Sara J. T. Guilcher

Bibliographic record

VenueDisability and Rehabilitation · 2019
Typereview
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoOntario Ministry of Health and Long-Term Care
KeywordsHip fractureMedicineAcute careConfusionInclusion (mineral)Health careTransitional careMEDLINERehabilitationNursingDischarge planningGrey literaturePsychologyPhysical therapy

Abstract

fetched live from OpenAlex

The results of this scoping review provide a useful foundation from which to build strategies to address challenges such as lack of information sharing, role confusion and disorganized discharge planning experienced by patients and caregivers during care transitions. Further research needs to explore the development of strategies to promote patient-centered care especially during discharge from an acute care facility.Implications for rehabilitationEncourage health care providers to collaborate with patients with hip fracture and caregivers on decision-making about rehabilitation and recovery goals, discharge planning and safe patient transfer.Assess the needs of patients with hip fracture and caregivers before, during and after a care transition to deliver patient and family-centered care across multiple care settings.Provide patients with hip fracture and caregivers standardized information-exchange tools to increase timely, accurate exchange of information during care transitions.Encourage formal discussions about roles and responsibilities in the transitions in care process among patients with hip fracture, caregivers and health care providers.

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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.466
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.346
Teacher spread0.323 · 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

Citations55
Published2019
Admission routes2
Has abstractyes

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