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Record W2074228368 · doi:10.1097/nur.0000000000000026

Why Clinical Change Leadership Is Essential for Project Success

2014· article· en· W2074228368 on OpenAlexaff
Teresa K. Gocsik, Amy J. Barton

Bibliographic record

VenueClinical Nurse Specialist · 2014
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsCentennial College
Fundersnot available
KeywordsWorkflowClinical nurse specialistProcess managementKey (lock)Knowledge managementQuality (philosophy)BusinessHealth careMedicinePsychologyNursingComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This is the first in a 4-part series where we will explore the role of the clinical nurse specialist (CNS) in the project lifecycle for effective technology adoption. The CNS is often called upon during major projects, such as the implementation of a new electronic health record, to play a key role in the project team. The CNS brings clinical knowledge and skills to the project team, with a particular focus on patient-centered workflows and the maintenance or improvement of the quality of care. However, CNSs may find it challenging to balance their role as members of the leadership group with the role they play in staff development and support.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.183
GPT teacher head0.402
Teacher spread0.219 · 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 designNot applicable
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

Citations7
Published2014
Admission routes1
Has abstractyes

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