Assessment of Human Factors After Advanced Life Support Courses Comparing Simulated Team and Real Team Assessment: A Randomized Controlled Cohort Trial
Bibliographic record
Abstract
Aim: Human factors are essential for high-quality resuscitation team collaboration and are, therefore, taught in international advanced life support courses, but their assessment differs widely. In Europe, the summative life support course assessment tests mainly adhere to guidelines but few human factors. This randomized controlled simulation trial investigated instructors' and course participants' perceptions of human factors assessment after two different summative assessments. Methods: All 5th/6th-year medical students who attended 19 advanced life support courses according to the 2015 European Resuscitation Council guidelines during one study year were invited to participate. Each course was randomized to either: (1) Simulated team assessment (one instructor simulates a team, and the assessed person leads this "team" through a cardiac-arrest scenario test); (2) Real team assessment (4 students form a team, one of them is assessed as the team leader; team members are not assessed and act only on team leader's commands). After the summative assessments, instructors, and students rated the tests' ability to assess human factors using a visual analog scale (VAS, 0 = no agreement, 10 = total agreement). Results: A total of 227 students participated in the 1-day Immediate Life Support courses, 196 students in the 2-day Advanced Life Support courses, additionally 54 instructors were included. Instructors judged all human factors significantly better in real team assessments; students rated leadership and situational awareness comparable between both assessments. Assessment pass rates were comparable between groups. Conclusion: Summative assessment in real teams was perceived significantly better to assess human factors. These results might influence current summative assessment practices in advanced life support courses.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".