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Record W3134869458 · doi:10.1177/0846537121993058

Creating a Competency-Based Medical Education Curriculum for Canadian Diagnostic Radiology Residency (Queen’s Fundamental Innovations in Residency Education)-Part 2: Core of Discipline Stage

2021· article· en· W3134869458 on OpenAlexaffabout
Siddharth Mishra, Andrew D. Chung, Christina Rogoza, Omar Islam, Benedetto Mussari, Xi Wang, Damon Dagnone, Nicholas Cofie, Nancy Dalgarno, Benjamin Y. M. Kwan

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's UniversityKingston Health Sciences Centre
Fundersnot available
KeywordsMedicineCore competencyCurriculumMedical educationMandateGraduate medical educationMentorshipResidency trainingRadiologyCompetence (human resources)Tracking (education)ManagementAccreditationPedagogyPsychology

Abstract

fetched live from OpenAlex

PURPOSE: All postgraduate residency programs in Canada are transitioning to a competency-based medical education (CBME) model divided into 4 stages of training. Queen's University has been the first Canadian institution to mandate transitioning to CBME across all residency programs, including Diagnostic Radiology. This study describes the implementation of CBME with a focus on the third developmental stage, Core of Discipline, in the Diagnostic Radiology residency program at Queen's University. We describe strategies applied and challenges encountered during the adoption and implementation process in order to inform the development of other CBME residency programs in Diagnostic Radiology. METHODS: At Queen's University, the Core of Discipline stage was developed using the Royal College of Physicians and Surgeons of Canada's (RCPSC) competence continuum guidelines and the CanMEDS framework to create radiology-specific entrustable professional activities (EPAs) and milestones for assessment. New committees, administrative positions, and assessment strategies were created to develop these assessment guidelines. Currently, 2 cohorts of residents (n = 6) are enrolled in the Core of Discipline stage. RESULTS: EPAs, milestones, and methods of evaluation for the Core of Discipline stage are described. Opportunities during implementation included tracking progress toward educational objectives and increased mentorship. Challenges included difficulty meeting procedural volume requirements, inconsistent procedural tracking, improving feedback mechanisms, and administrative burden. CONCLUSION: The transition to a competency-based curriculum in an academic Diagnostic Radiology residency program is significantly resource and time intensive. This report describes challenges faced in developing the Core of Discipline stage and potential solutions to facilitate this process.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.526
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.332
Teacher spread0.316 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations19
Published2021
Admission routes2
Has abstractyes

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