MétaCan
Menu
Back to cohort
Record W2461081635

The mini clinical evaluation exercise (mini-CEX) for assessing clinical performance of international medical graduates

2008· article· en· W2461081635 on OpenAlexaff
Balakrishnan Nair, Heather Alexander, Barry P. McGrath, Mulavana S Parvathy, Eve C Kilsby, Joshua I. Wenzel, Ian Frank, George Pachev, Gordon G. Page

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2008
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReliability (semiconductor)Observational studyMedical educationMedicineMetropolitan areaFamily medicinePsychology
DOInot available

Abstract

fetched live from OpenAlex

Abstract Objective: To evaluate the feasibility, reliability and acceptability of the mini clinical evaluation exercise (mini-CEX) for performance assessment among international medical graduates (IMGs). Design, setting and participants: Observational study of 209 patient encounters involving 28 IMGs and 35 examiners at three metropolitan teaching hospitals in New South Wales, Victoria and Queensland, September-December 2006. Main outcome measures: The reliability of the mini-CEX was estimated using generalisability (G) analysis, and its acceptability was evaluated by a written survey of the examiners and IMGs. Results: The G coefficient for eight encounters was 0.88, suggesting that the reliability of the mini-CEX was 0.90 for 10 encounters. Almost half of the IMGs (7/16) and most examiners (14/18) were satisfied with the mini-CEX as a learning tool. Most of the IMGs and examiners enjoyed the immediate feedback, which is a strong component of the tool. Conclusion: The mini-CEX is a reliable tool for performance assessment of IMGs, and is acceptable to and well received by both learners and supervisors.

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)0.021
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.004
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 head0.495
GPT teacher head0.607
Teacher spread0.113 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueGriffith Research Online (Griffith University, Queensland, Australia)Same topicGlobal Health Workforce IssuesFrench-language works237,207