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Record W2999503887 · doi:10.36834/cmej.67919

Black Ice: ways to get a grip on resident co-production within medical education change

2020· article· en· W2999503887 on OpenAlexaffvenueabout
Jeffery Damon Dagnone, Samantha Buttemer, Jena Hall, Liora Berger, Kristen Weersink

Bibliographic record

VenueCanadian Medical Education Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsSpecialtyEnthusiasmContext (archaeology)CurriculumQueen (butterfly)Medical educationMedicineNursingPolitical sciencePsychologyPedagogyFamily medicineHistory

Abstract

fetched live from OpenAlex

The Royal College of Physicians and Surgeons of Canada (RCPSC) is transforming its national approach to postgraduate medical education by transitioning all specialty programs to competency based medical education (CBME) curriculums over a seven-year period. Queen's University, with special permission from the RCPSC, launched CBME curricula for all incoming residents across its 29 specialty programs in July 2017. Resident engagement, empowerment, and co-production through this transition has been instrumental in successful implementation of CBME at Queen's University. This article aims to use our own experience at Queen's in the context of current literature and rooted in change leadership theory, to provide a guide for educators, learners, and institutions on how to leverage the interest and enthusiasm of trainees in the transition to CBME in postgraduate training. The following ten tips provides a model for avoiding the "black ice" type pitfalls that can arise with learner involvement and ensure a smoother transition for other institutions moving forward with 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)0.002
metaresearch head score (Gemma)0.107
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.107
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0360.002

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 head0.044
GPT teacher head0.357
Teacher spread0.312 · 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
GenreCommentary

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
Published2020
Admission routes3
Has abstractyes

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