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Record W2160350734 · doi:10.5430/jnep.v4n6p30

Transition of care and the impact on the environment of care

2014· article· en· W2160350734 on OpenAlexvenueno aff
Kerrie S. Guerrero, Susan E. Puls, Dorothy A. Andrew

Bibliographic record

VenueJournal of Nursing Education and Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicaidSAFERPsychological interventionMultidisciplinary approachHealth careTransitional careCommissionMedicineMedical emergencyNursingBusinessComputer science

Abstract

fetched live from OpenAlex

An unplanned readmission to the hospital within 30 days of discharge is seen as a failure by the healthcare team to appropriately plan for a safe and effective discharge to the next level of care. According to The Center for Medicare & Medicaid Services (CMS), the national average readmission rate in 2012 was 18.4%. As CMS shifts to a pay for performance strategy, a readmission rate higher than the national average for specific disease processes will result in a financial penalty. Many organizations have identified the need to improve the current discharge planning processes and to provide patients with a safer transition to the next level of care to prevent readmissions. Evidence demonstrates that there is value in reconfiguring the current discharge processes toward interventions that demonstrate a reduction in readmission rates. The discharge process should incorporate a multidisciplinary, multicomponent transition of care intervention that starts while the patient is in the hospital and continues with some type of home-care follow-up. Transition of care is a relatively new term that is used to describe a set of interventions designed to coordinate a patient’s care during the movement between healthcare settings. Implementing a well-designed transition of care program allows hospitals to provide a safe Environment of Care to patients during their care transitions. Environment of Care is a term coined by The Joint Commission that is used to describe the environment in which the patient is being cared for. The term usually involves three components: the people, the equipment and tools, and the building. As healthcare changes, it will become increasingly important to provide patients with care management throughout the continuum of care, which means thinking of the patient’s Environment of Care in much broader terms. How do transition of care processes affect the patient’s Environment of Care? Does a well-implemented transition of care program lead to a positive impact in the patient’s overall Environment of Care? This article provides an overview of how implementing a formalized transition of care process can lead to a safer Environment 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0080.004
Open science0.0010.011
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.100
GPT teacher head0.469
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2014
Admission routes1
Has abstractyes

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