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Record W2090263288 · doi:10.5430/jnep.v5n7p83

Development of a valid and reliable evaluation instrument for undergraduate nursing students during simulation

2015· article· en· W2090263288 on OpenAlexvenueno aff
Janeth J. Stiller, Kristine A. Nelson, Mindi Anderson, Mary Jane Ashe, Sharon T. Johnson, Kamal Sandhu, Ellen Mangold, Susan Scheid, Judy L. LeFlore

Bibliographic record

VenueJournal of Nursing Education and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistReliability (semiconductor)Cronbach's alphaDelphi methodContent validitySet (abstract data type)Test (biology)Educational measurementMedical educationDelphiNurse educationValidityPsychologyComputer scienceNursingMedicineCurriculumPsychometricsPedagogyClinical psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Background : Many nurse educators are using instruments to evaluate undergraduate nursing students' performance during simulation. Rigorous reliability and validity testing for such instruments is often not presented. Evaluation and testing of instruments used is needed to support objective measurement of student performance. Methods : This paper describes the development and testing of three new scenario-specific checklist instruments used for evaluating undergraduate nursing students' performance on an Objective Structured Clinical Examination using simulation. The new instruments were compared with original weighted ones previously used. An informal Delphi method was used to enhance content validity; Cronbach's alpha was utilized for reliability testing. Results : Although neither set of instruments had been used with trained raters, the new set of instruments performed better on reliability analysis. Conclusions : Checklist instruments may be more reliable in the objective measurement of student performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.040
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.096
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.302
GPT teacher head0.560
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designBench or experimental
DomainMethods
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

Citations2
Published2015
Admission routes1
Has abstractyes

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