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Utilizing Case-Based Learning and a Modified Lasater Clinical Judgment Rubric (LCJR) for Clinical Judgment Skills

2025· article· en· W4408164537 on OpenAlex
Bev Wilgenbusch, Rebekah Knauer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing Education Perspectives · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHamilton Health SciencesWiLAN (Canada)
Fundersnot available
KeywordsRubricClinical judgmentPsychologyMedical educationMathematics educationMedicineMedical physics

Abstract

fetched live from OpenAlex

It is challenging to assess clinical judgment skill development. The purpose of this study was to understand how case-based learning (CBL) and an innovation to the Lasater Clinical Judgment Rubric (LCJR) can be used to facilitate and assess clinical judgment skills. A pilot study using a quasi-experimental design was conducted to develop and assess clinical judgment competencies. Data were collected through a pretest/posttest, CBL worksheets, and modified LCJRs. Pretest/posttest results were statistically significant. Significant results were not shown by LCJR data as a whole, but one component demonstrated significant change. Considering the study's brevity, any significant LCJR results are promising.

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.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.814
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 integrity0.0000.001
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 head0.062
GPT teacher head0.495
Teacher spread0.433 · 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