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Record W2911875366 · doi:10.36834/cmej.43049

Strategies identified by program directors to improve adoption of the CanMEDS framework

2018· article· en· W2911875366 on OpenAlexafffundvenue
Isabelle Gaboury, Kathleen Ouellet, Marianne Xhignesse, Christina St‐Onge

Bibliographic record

VenueCanadian Medical Education Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de Sherbrooke
KeywordsFrenchContext (archaeology)Thematic analysisMedical educationLibrary scienceMedicineSociologyComputer scienceHumanitiesQualitative research

Abstract

fetched live from OpenAlex

Background: Challenges associated with the use of the CanMEDS physician competency framework (CanMEDS) have been the subject of several studies. Most of these have focused on the adoption of specific roles in an Anglophone context. This study aims to investigate how Francophone postgraduate medical education (PGME) program directors have integrated the CanMEDS framework into their programs.Methods: We invited Francophone PGME program directors to participate in group interviews aimed at exploring their experiences using the CanMEDS framework. We used an open-ended interview guide and realized a thematic analysis of the transcripts. Results: We held five group interviews between February and December 2014 with 17 Francophone program directors representing 13 out of a maximum of 62 different specialties/subspecialties. Although program directors endorsed the framework, its integration was seen as challenging, particularly the assessment of non-medical expert roles. To overcome these challenges, they relied on common strategies including a longitudinal approach to the framework, improving inter-program collaboration, and subcontracting the teaching of certain roles.Conclusion: While integrating the CanMEDS framework into their programs, Francophone program directors struggled with teaching and assessing non-medical expert roles and ensuring their longitudinal integration over time. Directors relied on various strategies, some of which (e.g., subcontracting) may ultimately limit the adoption of the framework as a whole.___Contexte: Les défis associés à l'utilisation du référentiel de compétences CanMEDS pour les médecins ont fait l'objet de plusieurs études. La plupart de ces études ont portées sur l'adoption de rôles spécifiques dans un contexte anglophone. Cette étude vise à explorer comment les directeurs de programmes d’études médicales postdoctorales (EMP) francophones ont intégré CanMEDS dans leurs programmes.Méthodes: Nous avons invité les directeurs de programmes EMP francophones à participer à des entrevues de groupe. Ces entrevues visaient à explorer leur expérience de l’utilisation du référentiel CanMEDS. Nous avons utilisé un guide d'entrevue ouvert et nous avons fait une analyse thématique des transcriptions. Résultats: Nous avons tenu cinq entrevues de groupe entre février et décembre 2014 avec 17 directeurs de programmes de 13 des 62 spécialités/sous-spécialités. Bien que les directeurs de programmes appuient le référentiel, son intégration a été perçue comme un défi, notamment en ce qui a trait à l'évaluation des rôles autres que celui d'expert médical. Pour surmonter ces défis, ils se sont appuyés sur des stratégies communes, notamment une approche longitudinale du référentiel, l'amélioration de la collaboration entre les programmes et la sous-traitance de l'enseignement de certains rôles.Conclusions: À travers le processus d’intégration du référentiel CanMEDS, les directeurs de programmes EMP francophones ont de la difficulté à enseigner et à évaluer les rôles autres que celui d'expert médical ainsi qu’à veiller à leur intégration respective et continue au fil du temps. Ils ont eu recours à diverses stratégies, dont certaines (p. ex., la sous-traitance) pourraient ultimement limiter l'adoption du référentiel dans son ensemble.

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)0.001
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient 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 score0.456
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.008
GPT teacher head0.339
Teacher spread0.331 · 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

Citations8
Published2018
Admission routes3
Has abstractyes

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