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Record W4283652817 · doi:10.36834/cmej.73889

Cancel culture: exploring the unintended consequences of cancelling the Canadian national licensing clinical examination

2022· article· en· W4283652817 on OpenAlexaffvenueabout
Claire Touchie, Debra Pugh

Bibliographic record

VenueCanadian Medical Education Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of OttawaOttawa HospitalMedical Council of Canada
Fundersnot available
KeywordsCertificationLicensureValuation (finance)HumanitiesPsychologyPolitical scienceMedicineMedical educationPhilosophyBusinessLawAccounting

Abstract

fetched live from OpenAlex

Assessment drives learning. However, when it comes to high-stakes examinations (e.g., for licensure or certification), these assessments of learning may be seen as unnecessary hurdles by some. Licensing clinical skills assessment in particular have come under fire over the years. Recently, assessments such as the Medical Council of Canada Qualifying Examination Part II, a clinical skills objective structured clinical examination, have been permanently cancelled. The authors explore potential consequences of this cancellation including those that are inadvertent and undesirable. Future next steps for clinical skills assessment are explored.

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)0.032
metaresearch head score (Gemma)0.140
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.140
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0140.008
Scholarly communication0.0060.004
Open science0.0040.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.085
GPT teacher head0.384
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2022
Admission routes3
Has abstractyes

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