Methods and Frameworks to Assess Operating Team Resilience: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: The operating room (OR) is a complex environment where errors significantly impact patient outcomes, and the ability of surgical teams to adapt and recover from unexpected disruptions-defined as resilience-is paramount. Frameworks offer structured approaches for analyzing resilience yet are variably applied throughout the relevant literature. This review aims to characterize how frameworks are used to study OR team resilience and examines the implications of inconsistent approaches. METHODS: After the Arksey & O'Malley framework, EMBASE, CINAHL, and MEDLINE were searched for studies published up to July 29, 2024. The search included keywords such as 'surgery' and 'resilience'. The included studies' reference lists were also manually searched. Studies focusing on the OR, examining the influence of human factors on team function and recovery, and reporting metrics for patient safety were included. Data extraction and content analysis were conducted independently by 2 reviewers, with results summarized narratively. RESULTS: Of 3165 studies identified, 9 met the inclusion criteria. Two utilized the systems engineering initiative for patient safety framework, and 2 incorporated the Oxford non-technical skills tool, whereas the remaining 5 developed an ad hoc approach to study operating team resilience. Notably, only 2 studies classified themselves as part of the resilience literature. CONCLUSIONS: This review demonstrates inconsistent framework application in surgical resilience research, resulting in methodological variability and limited cross-study synthesis. Developing frameworks specific to the OR is essential for advancing this field and improving study classification. Expanding search strategies to include resilience-adjacent terms will further enhance research visibility.
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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 | 0.114 | 0.298 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.095 | 0.067 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".