How we developed a comprehensive resuscitation-based simulation curriculum in emergency medicine
Bibliographic record
Abstract
Over the past decade, simulation-based education has emerged as a new and exciting adjunct to traditional bedside teaching and learning. Simulation-based education seems particularly relevant to emergency medicine training where residents have to master a very broad skill set, and may not have sufficient real clinical opportunities to achieve competence in each and every skill. In 2006, the Emergency Medicine program at Queen's University set out to enhance our core curriculum by developing and implementing a series of simulation-based teaching sessions with a focus on resuscitative care. The sessions were developed in such as way as to satisfy the four conditions associated with optimum learning and improvement of performance; appropriate difficulty of skill, repetitive practice, motivation, and immediate feedback. The content of the sessions was determined with consideration of the national training requirements set out by the Royal College of Physicians & Surgeons of Canada. Sessions were introduced in a stepwise fashion, starting with a cardiac resuscitation series based on the AHA ACLS guidelines, and leading up to a more advanced resuscitation series as staff became more adept at teaching with simulation, and as residents became more comfortable with this style of learning. The result is a longitudinal resuscitation curriculum that begins with fundamental skills of resuscitation and crisis resource management (CRM) in the first 2 years of residency and progresses through increasingly complex resuscitation cases where senior residents are expected to play a leadership role. This paper documents how we developed, implemented, and evaluated this resuscitation-based simulation curriculum for Emergency Medicine postgraduate trainees, with discussion of some of the challenges encountered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 teacher head, 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".