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Record W4391581556 · doi:10.5334/pme.960

Strategies to Enable Transformation in Medical Education: Faculty and Trainee Development in Competence By Design

2024· article· en· W4391581556 on OpenAlexaffabout
Adelle Atkinson, C. Michael Abbott, Anna Oswald, Andrée Boucher, Rodrigo B. Cavalcanti, Jason R. Frank, Linda Snell

Bibliographic record

VenuePerspectives on Medical Education · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversity of OttawaUniversité de MontréalUniversity of AlbertaUniversity Health NetworkUniversity of TorontoAlberta Medical AssociationRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsCompetence (human resources)Medical educationSpecialtyTransformative learningFaculty developmentSubspecialtyProfessional developmentMedicineEngineering managementPsychologyEngineeringPedagogy

Abstract

fetched live from OpenAlex

Transformative changes in health professions education need to incorporate effective faculty development, but few very large-scale faculty development designs have been described. The Royal College of Physicians and Surgeons of Canada's Competence by Design project was launched to transform the delivery of postgraduate medical education in Canada using a competency-based model. In this paper we outline the goals, principles, and rationale of the Royal College's national strategy for faculty and resident development initiatives to support the implementation of Competence by Design. We describe the activities and resources for both faculty and trainees that facilitated the redesign of training programs for each specialty and subspecialty at the national level, as well as supporting the implementation of the redesign at the local level. This undertaking was not without its challenges: we thus reflect on those challenges, enablers, and the lessons learned, and discuss a continuous quality improvement approach that was taken to iteratively inform the implementation process moving forward.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.372
Teacher spread0.353 · 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 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

Citations7
Published2024
Admission routes2
Has abstractyes

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