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Assessing the mini‐Clinical Evaluation Exercise in comparison to a national specialty examination

2006· article· en· W2079886100 on OpenAlexaffabout
Rose Hatala, Martha Ainslie, Barry O. Kassen, I C Mackie, James M. Roberts

Bibliographic record

VenueMedical Education · 2006
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsMedicineSpecialtyOral examinationCompetence (human resources)Objective structured clinical examinationPhysical examinationFamily medicineClinical clerkshipMedical educationInternal medicineCurriculumPsychology

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the reliability and validity of the Mini-Clinical Evaluation Exercise (mini-CEX) for postgraduate year 4 (PGY-4) internal medicine trainees compared to a high-stakes assessment of clinical competence, the Royal College of Physicians and Surgeons of Canada Comprehensive Examination in Internal Medicine (RCPSC IM examination). METHODS: Twenty-two PGY-4 residents at the University of British Columbia and the University of Calgary were evaluated, during the 6 months preceding their 2004 RCPSC IM examination, with a mean of 5.5 mini-CEX encounters (range 3-6). Experienced Royal College examiners from each site travelled to the alternate university to assess the encounters. RESULTS: The mini-CEX encounters assessed a broad range of internal medicine patient problems. The inter-encounter reliability for the residents' mean mini-CEX overall clinical competence score was 0.74. The attenuated correlation between residents' mini-CEX overall clinical competence score and their 2004 RCPSC IM oral examination score was 0.59 (P = 0.01). CONCLUSION: By examining multiple sources of validity evidence, this study suggests that the mini-CEX provides a reliable and valid assessment of clinical competence for PGY-4 trainees in internal medicine.

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)0.010
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.511
Teacher spread0.434 · 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.

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

Citations82
Published2006
Admission routes2
Has abstractyes

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