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Contextual Considerations in Summative Competency Examinations: Relevance to the Long Case

2005· article· en· W2020669594 on OpenAlexaffabout
John Turnbull, Jeff Turnbull, P.Grace Jacob, John Brown, Michel Duplessis, J. Rivest

Bibliographic record

VenueAcademic Medicine · 2005
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsRoyal College of Physicians and Surgeons of CanadaUniversity of OttawaWestern UniversityMcMaster University
Fundersnot available
KeywordsSummative assessmentCertificationContext (archaeology)Relevance (law)Medical educationDeliberationTest (biology)MedicinePsychometricsQuality assurancePsychologyReliability (semiconductor)Quality (philosophy)Formative assessmentClinical psychologyPedagogyPolitical sciencePathology

Abstract

fetched live from OpenAlex

Long-case patient-based examinations previously formed the basis of summative competency testing in physician certification examinations. These exams were found to be unreliable and have fallen from favor. During the authors' deliberation of the long case in the neurology certification examinations of the Royal College of Physicians and Surgeons of Canada, they considered the examination context and concluded that the appropriate psychometric analysis of the exams is highly contingent on the context. The examination context underlying certification examinations has evolved considerably; within a different context, a more cohesive test system based on a quality assurance framework could better manage substantive psychometric issues around case specificity, comprehensiveness, reliability, and compensability. These arguments are in small part psychometric, but are mostly philosophical and have relevance to the profession and the public.

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)0.003
metaresearch head score (Gemma)0.053
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.053
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0160.001

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 head0.125
GPT teacher head0.485
Teacher spread0.359 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations5
Published2005
Admission routes2
Has abstractyes

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