University of Ottawa’s Department of Emergency Medicine simulation boot camp: a descriptive review
Bibliographic record
Abstract
Emergency medicine (EM) residency programmes in Canada have recently introduced competency-based medical education (CBME), and the first stage of the curriculum focuses on standardising learner competency.1 Introductory residency boot camps provide a focused opportunity to address varying levels of medical knowledge and procedural competency prior to the start of residency.2–4 There are currently no papers that report on the landscape of EM orientation programmes outside of the American context. The Department of Emergency Medicine (DEM) at the University of Ottawa (uOttawa) offers one of Canada’s largest EM training programmes, and its curriculum includes a robust boot camp for incoming residents. The objective of this descriptive review is to describe uOttawa’s DEM resident boot camp curriculum. This will provide a framework for the development and refinement of introductory EM boot camps at other universities, which will help with the standardisation of learner competency prior to the start of residency. The uOttawa’s DEM boot camp was originally implemented in 2012 in response to a needs assessment identifying initial knowledge and skills necessary for starting EM residents. Based on continual feedback from instructors and participants, the curriculum has undergone several revisions to hone content, learning objectives and modes of educational delivery. The boot camp is delivered in July over 2 …
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".