Study protocol for a framework analysis using video review to identify latent safety threats: trauma resuscitation using in situ simulation team training (TRUST)
Bibliographic record
Abstract
INTRODUCTION: Errors in trauma resuscitation are common and have been attributed to breakdowns in the coordination of system elements (eg, tools/technology, physical environment and layout, individual skills/knowledge, team interaction). These breakdowns are triggered by unique circumstances and may go unrecognised by trauma team members or hospital administrators; they can be described as latent safety threats (LSTs). Retrospective approaches to identifying LSTs (ie, after they occur) are likely to be incomplete and prone to bias. To date, prospective studies have not used video review as the primary mechanism to identify any and all LSTs in trauma resuscitation. METHODS AND ANALYSIS: A series of 12 unannounced in situ simulations (ISS) will be conducted to prospectively identify LSTs at a level 1 Canadian trauma centre (over 800 dedicated trauma team activations annually). 4 scenarios have already been designed as part of this protocol based on 5 recurring themes found in the hospital's mortality and morbidity process. The actual trauma team will be activated to participate in the study. Each simulation will be audio/video recorded from 4 different camera angles and transcribed to conduct a framework analysis. Video reviewers will code the videos deductively based on a priori themes of LSTs identified from the literature, and/or inductively based on the events occurring in the simulation. LSTs will be prioritised to target interventions in future work. ETHICS AND DISSEMINATION: Institutional research ethics approval has been acquired (SMH REB #15-046). Results will be published in peer-reviewed journals and presented at relevant conferences. Findings will also be presented to key institutional stakeholders to inform mitigation strategies for improved patient safety.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".