Qualitative investigation of military surgical resuscitation teams: what are the drivers of success of a rapid response team?
Bibliographic record
Abstract
OBJECTIVES: This qualitative study explores the characteristics of a specialised military medical rapid response team (MRRT), the surgical resuscitation team (SRT). Despite mixed evidence of efficacy, civilian MRRTs are widely employed, with significant variation in structure and function. Recent increased use of these teams to mitigate patient risk in challenging healthcare scenarios, such as global pandemics, mass casualty events and resource-constrained health systems, mandates a reconceptualisation of how civilian MRRTs are created, trained and used. Here, we study the core functions and foundational underpinnings of SRTs and discuss how civilian MRRTs might learn from their military counterparts. DESIGN: Semistructured interview-based study using Descriptive Qualitative Research methodology and Thematic Analysis. SETTING: Remote audio interviews conducted via Zoom. PARTICIPANTS: Participants included 15 members of the United States Special Operations Command SRTs, representing all medical specialties of the SRT as well as operational planners. RESULTS: Adaptability was identified as a core function of SRTs and informed by four foundational underpinnings: mission variability, shared values and principles, interpersonal and organisational trust and highly effective teaming. Our findings provide three important insights for civilian MRRTs: (1) team member roles should not be defined by silos of professional specialisation, (2) trust is a key factor in the teaming process and (3) team principles and values result in and are reinforced by organisational trust. CONCLUSION: This study offers the first in-depth investigation of a unique military MRRT. Important insights that may offer benefit to civilian MRRT practices include enabling the breakdown of traditional division of labour, allowing for and promoting deep interpersonal and professional familiarity, and facilitating a cycle of positive reinforcement between teams and organisations. Future investigation of small team limitations, comparability to civilian MRRTs, and the team relationship to the larger organisation are needed to better understand how these teams function in a healthcare system and translate to civilian practice.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".