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Record W2120650221 · doi:10.4300/jgme-06-04-47

Updating CanMEDS in 2015: Ensuring Quality in the Change Process

2014· article· en· W2120650221 on OpenAlexaffabout
Elizabeth M. Wooster, Elaine Van Melle, Ming-Ka Chan, Deepak Dath, Jonathan Sherbino, Jason R. Frank

Bibliographic record

VenueJournal of Graduate Medical Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of OttawaUniversity of ManitobaQueen's UniversityMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineMedical educationUrologyProcess (computing)Qualitative research

Abstract

fetched live from OpenAlex

Background: The Royal College of Physicians and Surgeons of Canada (RCPSC) will launch CanMEDS 2015 after a 3-year process to revise and refine the CanMEDS 2005 Framework. To engage multiple stakeholders, the RCPSC distributed an online survey to its fellows, trainees, and other interested stakeholders in 2013.Methods: Educators at the RCPSC developed a survey using an iterative, reflective process. The mixed methods survey, administered electronically, consisted of open- and close-ended questions related to the 7 CanMEDS Roles. The results were analyzed by 2 RCPSC clinician educators and 2 RCPSC education scientists.Results: Of 1204 respondents, 60% were fellows and 19% were trainees, representing all but 2 disciplines within the RCPSC. Forty-seven percent were university-based and 64% considered themselves to either know the framework or be an expert in applying 1 role. The depth and breadth of responses varied greatly and qualitative analysis revealed 5 overarching themes: simplify the descriptions of roles; clarify the roles; reduce overlap between roles; assist with implementation; and include patient safety.Conclusions: Stakeholders are very interested in participating in the revision and renewal of the CanMEDS Framework. Responses align with themes found through other consultation methods and validate the importance of reaching out to stakeholders in the process of framework revision.

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)0.026
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.018
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)0.0000.000

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 head0.386
GPT teacher head0.615
Teacher spread0.230 · 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 designObservational
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
Published2014
Admission routes2
Has abstractyes

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