Team leadership assessment after advanced life support courses comparing real teams vs. simulated teams
Bibliographic record
Abstract
Aim: Effective team leadership is essential during cardiopulmonary resuscitation (CPR) and is taught during international advanced life support (ALS) courses. This study compared the judgement of team leadership during summative assessments after those courses using different validated assessment tools while comparing two different summative assessment methods. Methods: After ALS courses, twenty videos of simulated team assessments and 20 videos of real team assessments were evaluated and compared. Simulated team assessment used an instructor miming a whole team, whereas real team assessment used course participants as a team that acted on the team leader's commands. Three examiners individually evaluated each video on four different validated team leadership assessment tools and on the original European Resuscitation Council's (ERC) scenario test assessment form which does not assess leadership. The primary outcome was the average performance summary score between all three examiners for each assessment method. Results: = 0.569), meaning that participants of both assessments performed equally. Conclusion: Team leadership performance is rated significantly higher in real team summative assessments after ALS courses compared to simulated team assessments by four leadership assessment tools but not by the standard ERC's scenario test assessment form. These results suggest that summative assessments in ALS courses should integrate real team assessments, and a new assessment tool including an assessment of leadership skills needs to be developed.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".