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Record W3030272337 · doi:10.1080/0142159x.2020.1767768

A systems approach for institutional CBME adoption at Queen’s University

2020· article· en· W3030272337 on OpenAlexaffabout
Denise Stockley, Rylan Egan, Richard van Wylick, Amber Hastings Truelove, Laura April McEwen, Damon Dagnone, Ross Walker, Leslie Flynn, Richard Reznick

Bibliographic record

VenueMedical Teacher · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsExcellenceSpecialtyMedicineMedical educationCompetence (human resources)AccreditationManagementFamily medicinePolitical science

Abstract

fetched live from OpenAlex

The Royal College of Physicians and Surgeons of Canada (RCPSC) has begun the transition to Competency by Design (CBD), a new curricular model for residency education that 'ensure[s] competence, but teaches for excellence'. By 2022, all Canadian specialty programs are anticipated to have completed the CBD cohort process which includes workshops facilitated by a Royal College Clinician Educator. Queen's University in Ontario, Canada, was granted approval by the RCPSC to embark upon an accelerated path to competency-based medical education (CBME) for all our postgraduate specialties. This accelerated path allowed us to take an institutional approach for CBME implementation and ensure that all specialities were part of a system-wide change. Our unique institution-wide approach to CBD is the first of its kind across Canada. From both a theoretical and practical perspective we undertook CBME using a systems approach that allowed us to build the foundations for CBME, implement the change, and plan for sustainability. This has created opportunities to bridge and connect the various programs involved in the implementation of CBME on Queen's campus. The systems approach was an essential part of our strategy to develop a community dedicated to ensuring a successful CBME implementation.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.287
Teacher spread0.246 · 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 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

Citations25
Published2020
Admission routes2
Has abstractyes

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