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Record W1603860875 · doi:10.5334/ijic.1195

Information sharing with rural family caregivers during care transitions of hip fracture patients

2014· article· en· W1603860875 on OpenAlexaffabout
Jacobi Elliott, Dorothy Forbes, Bert M. Chesworth, Christine Ceci, Paul Stolee

Bibliographic record

VenueInternational Journal of Integrated Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsWestern UniversityUniversity of AlbertaUniversity of Waterloo
Fundersnot available
KeywordsHip fractureInformation sharingMedicineNursingOsteoporosisComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

INTRODUCTION: Following hip fracture surgery, patients often experience multiple transitions through different care settings, with resultant challenges to the quality and continuity of patient care. Family caregivers can play a key role in these transitions, but are often poorly engaged in the process. We aimed to: (1) examine the characteristics of the family caregivers' experience of communication and information sharing and (2) identify facilitators and barriers of effective information sharing among patients, family caregivers and health care providers. METHODS: Using an ethnographic approach, we followed 11 post-surgical hip fracture patients through subsequent care transitions in rural Ontario; in-depth interviews were conducted with patients, family caregivers (n = 8) and health care providers (n = 24). RESULTS: Priority areas for improved information sharing relate to trust and respect, involvement, and information needs and expectations; facilitators and barriers included prior health care experience, trusting relationships and the rural setting. CONCLUSION: As with knowledge translation, effective strategies to improve information sharing and care continuity for older patients with chronic illness may be those that involve active facilitation of an on-going partnership that respects the knowledge of all those involved.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.292
Teacher spread0.278 · 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 designQualitative
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

Citations43
Published2014
Admission routes2
Has abstractyes

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