Trauma Resuscitation Using in situ Simulation Team Training (TRUST) study: latent safety threat evaluation using framework analysis and video review
Bibliographic record
Abstract
INTRODUCTION: Trauma resuscitation is a complex and time-sensitive endeavour with significant risk for error. These errors can manifest from sequential system, team and knowledge-based failures, defined as latent safety threats (LSTs). In situ simulation (ISS) provides a novel prospective approach to recreate clinical situations that may manifest LSTs. Using ISS coupled with a human factors-based video review and modified framework analysis, we sought to identify and quantify LSTs within trauma resuscitation scenarios. METHODS: At a level 1 trauma centre, we video recorded 12 monthly unannounced ISS to prospectively identify trauma-related LSTs. The on-call multidisciplinary trauma team participated in the study. Using a modified framework analysis, human factors experts transcribed and coded the videos. We identified LST events, categorised them into themes and subthemes and used a hazard matrix to prioritise subthemes requiring intervention. RESULTS: We identified 843 LST events during 12 simulations, categorised into seven themes and 38 subthemes, of which 23 are considered critical. The seven themes relate to physical workspace, mental model formation, equipment, unclear accountability, demands exceeding individuals' capacity, infection control and task-specific issues. The physical workspace theme accounted for the largest number of critical LST events (n=152). We observed differences in LST events across the four scenarios; complex scenarios had more LST events. CONCLUSIONS: We identified a diverse set of critical LSTs during trauma resuscitations using ISS coupled with video-based framework analysis. The hazard matrix scoring, in combination with detailed LST subthemes, supported identification of critical LSTs requiring intervention and enhanced efforts intended to improve patient safety. This approach may be useful to others who seek to understand the contributing factors to common LSTs and design interventions to mitigate them.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".