Integrating training, practice, and reflection within a new model for Canadian medical licensure: a concept paper prepared for the Medical Council of Canada
Bibliographic record
Abstract
In 2020 the Medical Council of Canada created a task force to make recommendations on the modernization of its practices for granting licensure to medical trainees. This task force solicited papers on this topic from subject matter experts. As outlined within this Concept Paper, our proposal would shift licensure away from the traditional focus on high-stakes summative exams in a way that integrates training, clinical practice, and reflection. Specifically, we propose a model of graduated licensure that would have three stages including: a trainee license for trainees that have demonstrated adequate medical knowledge to begin training as a closely supervised resident, a transition to practice license for trainees that have compiled a reflective educational portfolio demonstrating the clinical competence required to begin independent practice with limitations and support, and a fully independent license for unsupervised practice for attendings that have demonstrated competence through a reflective portfolio of clinical analytics. This proposal was reviewed by a diverse group of 30 trainees, practitioners, and administrators in medical education. Their feedback was analyzed and summarized to provide an overview of the likely reception that this proposal would receive from the medical education community.
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 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 | 0.040 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.026 |
| Scholarly communication | 0.025 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".