Canadian urology programs can be leaders in competency-based education
Bibliographic record
Abstract
Accreditation Council for Graduate Medical Education (ACGME)'s Marvin Dunn's visit to the RCPSC in 1997 led to the development of the ACGME's Six Competencies model.Canadian urologists have written to the RCPSC for assistance with presenting CanMEDS at international meetings.CanMEDS, developed by Canadian physicians, is hugely influential and worthy of pride.Mickelson and MacNeily are critical of the implementation of CanMEDS, describing it as "nebulous," "poorly defined," "abstract" and with a "paucity of tools to teach them."These are among several unsubstantiated misunderstandings about CanMEDS in the article.These same criticisms have also been levelled at the ACGME competencies and others used around the world.Rather, there are 2 fundamental truths to consider about the implementation of CanMEDS in Canadian programs: 1) many of these competencies have always been taught and assessed but need to be made explicit; and 2) this competency-based approach requires each specialty to clearly define what the CanMEDS roles mean for their practice.The authors confuse the inherently generic nature of the CanMEDS competencies framework for lack of clarity.CanMEDS is a framework that must be adaptable by all the College's 62 disciplines.In fact, thanks to the hard work and dedication of Canadian physicians and surgeons, most of the RCPSC specialty committees have successfully defined specific and comprehensive standards that define what it means to be competent in their specialty using CanMEDS.These are publicly available at http://rcpsc.medical.org/information/index.php.The approximately 700 specialty programs have all been visited by peer reviewers as part of the accreditation cycle, and all have implemented CanMEDS in the programs in a variety of ways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".