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Referral to and discharge from cardiac rehabilitation: key informant views on continuity of care

2006· article· en· W2084450348 on OpenAlexafffund
Sherry L. Grace, Suzan Krepostman, Dina Brooks, Susan Jaglal, Beth L. Abramson, Pat Scholey, Neville Suskin, Heather M. Arthur, Donna E. Stewart

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2006
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsTrillium Health CentreWestern UniversitySt. Michael's HospitalInstitute for Clinical Evaluative SciencesUniversity Health NetworkToronto General HospitalUniversity of TorontoMcMaster UniversityLondon Health Sciences CentreWomen's College Hospital
FundersCanadian Institutes of Health Research
KeywordsReferralRehabilitationContinuity of careNursingMedicineGrounded theoryHospital dischargePrimary careKey (lock)Health careFamily medicineQualitative researchPsychologyMedical educationPhysical therapyIntensive care medicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the system-level barriers and facilitators of continuity of care from acute care to cardiac rehabilitation (CR), and from CR discharge to follow-up with primary health care providers. METHOD: Semi-structured individual interviews with 24 key informants including CR staff, research scientists, policy makers, cardiologists and other doctors from a regional to international level were conducted regarding the processes of referral to and discharge from cardiac rehabilitation. Key informant interviews were audio taped, transcribed, and imported into QSR N6 software for Grounded analysis. RESULTS: Themes that emerged related to communication, referral and discharge processes, health care provider practices, inter- and intra-institutional relationships, and alternative models of delivery to improve continuity. CONCLUSIONS: Ramifications for enhancing referral of patients to beneficial CR services and follow-up by primary care providers to ensure maintenance of functional and health-related gains are discussed.

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)0.012
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.097
GPT teacher head0.530
Teacher spread0.433 · 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 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

Citations44
Published2006
Admission routes2
Has abstractyes

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