Assessing Team Performance: A Mixed-Methods Analysis Using Interprofessional in situ Simulation
Bibliographic record
Abstract
Introduction: Optimizing the performance of emergency department (ED) teams impacts patient care, but the utility of current, team-based performance assessment tools to comprehensively measure this impact is underexplored. In this study we aimed to 1) evaluate ED team performance using current team-based assessment tools during an interprofessional in situ simulation and 2) identify characteristics of effective ED teams. Methods: This mixed-methods study employed case study methodology based on a constructivist paradigm. Sixty-three eligible nurses, technicians, pharmacists, and postgraduate year 2–4 emergency medicine residents at a tertiary academic ED participated in a 10-minute in situ simulation of a critically ill patient. Participants self-rated performance using the Team Performance Observation Tool (TPOT) 2.0 and completed a brief demographic form. Two raters independently reviewed simulation videos and rated performance using the TPOT 2.0, Team Emergency Assessment Measure (TEAM), and Ottawa Crisis Resource Management Global Rating Scale (Ottawa GRS). Following simulations, we conducted semi-structured interviews and focus groups with in situ participants. Transcripts were analyzed using thematic analysis. Results: Eighteen team-based simulations took place between January–April 2021. Raters’ scores were on the upper end of the tools for the TPOT 2.0 (R1 4.90, SD 0.17; R2 4.53, SD 0.27, IRR [inter-rater reliability] 0.47), TEAM (R1 3.89, SD 0.19; R2 3.58, SD 0.39, IRR 0.73), and Ottawa GRS (R1 6.6, SD 0.56; R2 6.2, SD 0.54, IRR 0.68). We identified six themes from our interview data: team member entrustment; interdependent energy; leadership tone; optimal communication; strategic staffing; and simulation empowering team performance. Conclusion: Current team performance assessment tools insufficiently discriminate among high performing teams in the ED. Emergency department-specific assessments that capture features of entrustability, interdependent energy, and leadership tone may offer a more comprehensive way to assess an individual’s contribution to a team’s performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".