Using a situational awareness global assessment technique for interprofessional obstetrical team training with high fidelity simulation
Bibliographic record
Abstract
Evidence suggests that breakdowns in communication and a lack of situation awareness contribute to poor performance of medical teams. In this pilot study, three interprofessional obstetrical teams determined the feasibility of using the situation awareness global assessment technique (SAGAT) during simulated critical event management of three obstetrical scenarios. After each scenario, teams were asked to complete questionnaires assessing their opinion of how their performance was affected by the introduction of questions during a SAGAT stop. Fifteen obstetrical professionals took part in the study and completed the three scenarios in teams consisting of five members. At nine questions per stop, more participants agreed or strongly agreed that there were too many questions per stop (57.1%) than when we asked six questions per stop (13%) and three questions per stop (0%). A number of interprofessional differences in response to this interprofessional experience were noted. A team SAGAT score was determined by calculating the proportion of correct responses for each individual. Higher scores were associated with better adherence to outcome times, although not statistically significant. A robust study design building on our pilot data is needed to probe the differing interprofessional perceptions of SAGAT and the potential association between its scores and clinical outcome times.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".