The team briefing: setting up relational coordination for your resuscitation
Bibliographic record
Abstract
[Extract]<br/>“Ring. Ring…” You watch the charge nurse, James, pick up the paramedic patch phone. He listens intently then replies, “alright, see you soon.” James tells you that paramedics are ten minutes out with a 77-year-old male driver from a motor vehicle collision at highway speeds. The patient has obvious head and chest injuries, is GCS 13 and hemodynamically stable. The trauma alert is activated and the team starts to gather…how do you prepare?<br/>The concept of briefing resuscitation teams is not novel, but there has been renewed energy towards improving those briefings as better understanding of team behaviours and non-clinical resuscitation skills have emerged [1-4]. Effective briefings have reduced mortality in surgical patients and have been shown to reduce the time to critical team tasks within a simulated resuscitation environment [5,6]. One proposed mechanism for benefit is the construction of a “shared mental model” amongst team members [2,4]. In a recent publication in Clinical and Experimental Emergency Medicine, Reid et al. advocated for the incorporation of the “zero-point survey” (ZPS) as an antecedent to the longstanding and familiar primary survey. The ZPS includes consideration of personal and environmental factors as well as a critical team component, the briefing [4]. The authors associated with the ZPS humbly acknowledged a lack of empirical evidence for which to support their approach. We thought we might further explore and support the theoretical footing.<br/>In this commentary we apply a macro-organizational theory, relational coordination, to a micro-level issue, caring for a single patient, to explore team briefings. We use experience from a large ethnographic study of a trauma service to support the concept of team briefings in the resuscitation setting for reasons that relate to, but go beyond, the “shared mental model.”
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".