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Record W4405239331 · doi:10.1097/nne.0000000000001790

Development of Clinical Judgment in Prelicensure Nursing Students Through Simulation

2024· article· en· W4405239331 on OpenAlexaff
Michelle E. Bussard, Lisa K. Jacobs, Sarah Mahoney, L G Davis, Annette M. Oberhaus, Patrick Lavoie

Bibliographic record

VenueNurse Educator · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRubricClinical judgmentBachelorCurriculumPsychologyMedical educationLongitudinal studyNursingMedicineMathematics educationMedical physicsPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: There is a lack of longitudinal studies measuring student progression in clinical judgment. Previous studies measured gains in clinical judgment after 1 intervention or over 1 semester. PURPOSE: This study evaluated the development and progression of clinical judgment in simulation throughout a prelicensure bachelor of science in nursing (BSN) program using the Lasater Clinical Judgment Rubric (LCJR). METHODS: This retrospective longitudinal study evaluated clinical judgment using the LCJR over 4 semesters, with a sample of 53 prelicensure nursing students. Each student had 18 simulation scores recorded during the study. A linear mixed model was employed to compare LCJR average scores from the beginning to the end of the program and across each semester. RESULTS: Eighteen simulations were reviewed among 4 nursing courses. LCJR scores increased progressively from the first to the fourth semester. CONCLUSIONS: Students showed progression of clinical judgment throughout a BSN curriculum using the LCJR as an assessment tool.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.148
GPT teacher head0.563
Teacher spread0.415 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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