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Record W2317770203 · doi:10.1097/ncm.0b013e318198d4be

From Hospital to Home After Cardiac Surgery

2009· article· en· W2317770203 on OpenAlexaff
Diane Morin, Michèle Aubin, Lucie Vézina, Johanne Gagnon, Sandra Racine, Daniel Reinharz, Michele Paradis, Clémence Dallaire, Karine Aubin

Bibliographic record

VenueProfessional Case Management · 2009
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineMEDLINEPolitical science

Abstract

fetched live from OpenAlex

PURPOSE/OBJECTIVES: This quasi-experimental research aims to (1) evaluate the implementation process of a community nursing care management model and (2) assess the effects of this model on patients followed at home. PRIMARY PRACTICE SETTING: Two community healthcare centers had introduced a community nursing care management model in their practice (experimental groups), whereas another health community care center with no experience with such a model served as a control group. The community nursing care management model included clinical pathways designed for a clientele who had been hospitalized for cardiac surgery. FINDINGS/CONCLUSIONS: Even though the implementation process was challenging, the community nursing care management model was found useful enough to be integrated into routine nursing home care practice after cardiac surgery. Although the effects produced by this systematic home care program on the clientele did not differ significantly from those produced by usual nursing care, there was a positive effect for the clientele recorded on all measurement indicators used. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: The introduction of the nursing care management model enabled nurses to structure the care provided and reduced interindividual variation. The application of this program also proved to be an opportunity to initiate and assimilate new professional roles. Additional studies should be conducted to assess its effectiveness in home care for other health problems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.619

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 integrity0.0000.000
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 head0.009
GPT teacher head0.295
Teacher spread0.285 · 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 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

Citations5
Published2009
Admission routes1
Has abstractyes

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