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Record W2104256801 · doi:10.1177/0894318414558618

Theory to Practice

2014· article· en· W2104256801 on OpenAlexaff
Karen L. Ursel

Bibliographic record

VenueNursing Science Quarterly · 2014
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsNursingExcellenceNursing theoryNurse educationSpecialtyHealth careNursing researchPsychologyMedicineMEDLINEMedical educationEpistemology

Abstract

fetched live from OpenAlex

Guided by the humanbecoming leading-following model, the author designed a nursing theories course with the intention of creating a meaningful nursing theory to practice link. The author perceived that with the implementation of Situation-Background-Assessment-Recommendations (SBAR) communication, nursing staff had drifted away from using the Kardex™ in shift to shift reporting. Nurse students, faculty, and staff members supported the creation of a theories project which would engage nursing students in the pursuit of clinical excellence. The project chosen was to revise the existing Kardex™ (predominant nursing communication tool). In the project, guided by a nursing theory, nursing students focused on the unique patient's experience, depicting the specific role of nursing knowledge and the contributions of the registered nurse to the patient's healthcare journey. The emphasis of this theoretical learning was the application of a nursing theory to real-life clinical challenges with communication of relevant, timely, and accurate patient information, recognizing that real problems are often complex and require multi-perspective approaches. This project created learning opportunities where a nursing theory would be chosen by the nursing student clinical group and applied in their clinical specialty area. This practice activity served to broaden student understandings of the role of nursing knowledge and nursing theories in their professional practice.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.338
Teacher spread0.329 · 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 designOther design
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

Citations0
Published2014
Admission routes1
Has abstractyes

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