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Record W2107330557 · doi:10.5334/ijic.1103

“Just another fish in the pond”: the transitional care experience of a hip fracture patient

2013· article· en· W2107330557 on OpenAlexaff
Justine Toscan, Brooke Manderson, Paul Stolee

Bibliographic record

VenueInternational Journal of Integrated Care · 2013
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCompromiseHealth careTransitional careNursingPerspective (graphical)MedicineFocus groupPsychologyBusiness

Abstract

fetched live from OpenAlex

INTRODUCTION: Miscommunication and lack of coordination can compromise care quality and patient safety during transitions in care, especially for medically complex older adults. Little research has been done to investigate care transitions from the perspective of those receiving and providing care. METHODS: This study explored multiple care transitions for an elderly hip fracture patient, post-surgery. Interviews and observations were conducted with the patient, their family caregivers, and health care providers, at each point of transition between four different care settings. RESULTS: FOUR KEY THEMES WERE IDENTIFIED OVER THE PATIENTS CARE TRAJECTORY: 'Missing Crucial Coversations'-Patient and family caregivers did not feel involved or informed about decisions in care; 'Who's Who'-Confusion about the role of health care providers; 'Ready or Not'-Not knowing what to expect or what is expected; and, 'Playing by the Rules'-Health system policies and procedures hinder individualized care. CONCLUSION: Study findings point to the need for the health care system to engage patients and family caregivers more fully and consistently in the process of care transitions as well as the importance of understanding these processes from multiple perspectives. Recommendations for system integration are proposed with a focus on transitional 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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.

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

Citations48
Published2013
Admission routes1
Has abstractyes

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