P.100 A competency-based stroke curriculum for non-neurologists
Bibliographic record
Abstract
Background: Previously-identified deficiencies in stroke training for emergency and internal medicine trainees led us to develop a competency-based curriculum for a stroke rotation, based upon entrusbable professional activities (EPAs). EPAs are observable and measurable activities that are routine care within a given medical specialty. Methods: We surveyed stroke- and non-stroke neurologists using a modified Delphi process with two iterations. The survey sought input on the number and nature of EPAs considered most important and achievable during a one month stroke rotation. Results: Surveyed neurologists considered 5-10 EPAs as adequate and reasonable to achieve during a one month elective. A list of the most essential EPAs was obtained and will be used as the basis of a curriculum for rotating residents in Internal and Emergency medicine at the Island Medical Program in Victoria, BC. Conclusions: Our work highlights an approach to meeting an identified gap in resident training in an important area of neurology (stroke). A competency based approach to medical education, focusing on EPAs, offers an innovative way of approaching resident education that seeks to ensure residents develop skills that experts in the field have identified as most essential for the work at hand (in this case, the proper management of stroke patients).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".