Improving the relational aspects of trauma care through translational simulation
Bibliographic record
Abstract
BACKGROUND: Major trauma care is complex and requires individuals and teams to perform together in time critical, high-stakes situations. Scenario-based simulation is well established as a strategy for trauma teamwork improvement, but its role in the relational and cultural aspects of trauma care is less well understood. Relational coordination theory offers a framework through which we aimed to understand the impact of an established trauma simulation programme. METHODS: We studied simulation activities using a narrative survey of trauma providers from anaesthesia, emergency medicine, medical imaging, surgery, trauma service, intensive care, and pre-hospital providers at Gold Coast University Hospital, in conjunction with data from an ethnography. Data analysis was performed using a recursive approach-a simultaneous deductive approach using the relational coordination framework and an inductive analysis. RESULTS: Ninety-five of 480 (19.8%) staff completed free-text survey questions on simulation. Deductive analysis of data from these narrative survey results using the RC framework domains identified examples of shared goals, shared knowledge, communication and mutual respect. Two major themes from the inductive analysis-"Behaviour, process and system change" and "Culture and relationships"-aligned closely with findings from the RC analysis, with additional themes of "Personal and team learning" and the "Impact of the simulation experience" identified. CONCLUSIONS: Our findings suggest that an established trauma simulation programme can have a profound impact on the relational aspects of care and the development of a collaborative culture, with perceived tangible impacts on teamwork behaviours and institutional systems and processes. The RC framework-shared knowledge, shared goals and mutual respect in the context of communication that is timely, accurate, frequent and problem-solving based-can provide a common language for simulation educators to design and debrief simulation exercises that aim to have a translational impact.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".