A Set of Principles, Developed by Residents, to Guide Canadian Residency Education
Bibliographic record
Abstract
With so much invested in the clinical competency of physicians, adequate and appropriate mechanisms are needed to ensure that educational systems provide the highest-quality training possible and are responsive both to the changing demands of the patient population and to changing technologies and research. After a literature review, the authors concluded that there are no established criteria or principles, from a learners' perspective, that set out goals for the delivery and evaluation in Canada of quality postgraduate medical education. The authors initiated the process of developing a set of principles of medical education based on residents' perspectives by compiling a list of issues and concepts that were felt to be important to creating the "ideal" postgraduate medical education system. This list of issues was divided into broad categories before presentation by the authors for Canada-wide discussion, reflection, and further refinement of concepts and issues across a nine-month period. The process eventually resulted in the final consensus-driven and iterative development of the main categories and the final principles that were adopted by the Canadian Association of Internes and Residents (CAIR). The authors present this set of principles and propose that they be used as a template to guide postgraduate medical education and against which changes to the system can be evaluated. CAIR will use these principles in a number of ways, including evaluation, education, and quality assurance.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".