Pediatric Emergency Medicine Fellows Education Day: Addressing CanMEDS objectives at a national subspecialty conference
Bibliographic record
Abstract
Canadian subspecialty residency training programs are developed around the learning objectives listed in the seven Canadian Medical Education Directives for Specialists (CanMEDS) criteria. Delivering content on objectives outside of those traditionally acquired in clinical rotations can be a challenge. In the present article, the planning process, curriculum development, and evaluation and assessment of a national subspecialty conference model in providing CanMEDS objective-based content sessions in the categories other than Medical Expert (Professional, Scholar, Communicator, Collaborator, Manager and Health Advocate) is described. It is hypothesized that the development of a CanMEDS objective-based curriculum would be positively received by subspecialty residents attending this conference. Attendees of sessions in a two-year curriculum cycle assessed the content as valuable, relevant and effective. The application of this process can be useful to other subspecialty residency training programs to meet the needs of their CanMEDS objective-based training requirements.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".