Use of Human Patient Simulation and the Situation Awareness Global Assessment Technique in Practical Trauma Skills Assessment
Bibliographic record
Abstract
BACKGROUND: Situation awareness (SA) is defined as the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. This construct is vital to decision making in intense, dynamic environments. It has been used in aviation as it relates to pilot performance, but has not been applied to medical education. The most widely used objective tool for measuring trainee SA is the Situation Awareness Global Assessment Technique (SAGAT). The purpose of this study was to design and validate SAGAT for assessment of practical trauma skills, and to compare SAGAT results to traditional checklist style scoring. METHODS: Using the Human Patient Simulator, we designed SAGAT for practical trauma skills assessment based on Advanced Trauma Life Support objectives. Sixteen subjects (four staff surgeons, four senior residents, four junior residents, and four medical students) participated in three scenarios each. They were assessed using SAGAT and traditional checklist assessment. A questionnaire was used to assess possible confounding factors in attaining SA and overall trainee satisfaction. RESULTS: SAGAT was found to show significant difference (analysis of variance; p < 0.001) in scores based on level of training lending statistical support to construct validity. SAGAT was likewise found to display reliability (Cronbach's alpha 0.767), and significant scoring correlation with traditional checklist performance measures (Pearson's coefficient 0.806). The questionnaire revealed no confounding factors and universal satisfaction with the human patient simulator and SAGAT. CONCLUSIONS: SAGAT is a valid, reliable assessment tool for trauma trainees in the dynamic clinical environment created by human patient simulation. Information provided by SAGAT could provide specific feedback, direct individualized teaching, and support curriculum change. Introduction of SAGAT could improve the current assessment model for practical trauma education.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".