Emergency medicine resident crisis resource management ability: a simulation-based longitudinal study
Bibliographic record
Abstract
BACKGROUND: Simulation has been identified as a means of assessing resident physicians' mastery of technical skills, but there is a lack of evidence for its utility in longitudinal assessments of residents' non-technical clinical abilities. We evaluated the growth of crisis resource management (CRM) skills in the simulation setting using a validated tool, the Ottawa Crisis Resource Management Global Rating Scale (Ottawa GRS). We hypothesized that the Ottawa GRS would reflect progressive growth of CRM ability throughout residency. METHODS: Forty-five emergency medicine residents were tracked with annual simulation assessments between 2006 and 2011. We used mixed-methods repeated-measures regression analyses to evaluate elements of the Ottawa GRS by level of training to predict performance growth throughout a 3-year residency. RESULTS: Ottawa GRS scores increased over time, and the domains of leadership, problem solving, and resource utilization, in particular, were predictive of overall performance. There was a significant gain in all Ottawa GRS components between postgraduate years 1 and 2, but no significant difference in GRS performance between years 2 and 3. CONCLUSIONS: In summary, CRM skills are progressive abilities, and simulation is a useful modality for tracking their development. Modification of this tool may be needed to assess advanced learners' gains in performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".