Team cognition in healthcare simulation: a framework for deliberate measurement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Team mental models and team situational awareness are key components of healthcare team simulation. Human factors and organizational psychology researchers have developed clear definitions and theories about these concepts that are at times 'lost in translation' within the prevailing forms of measurement and training utilized in healthcare. Simulation research to date has often relied upon indirect and imprecise measures and a conceptualization of team cognition that ill equips simulation educators as they endeavour to optimize healthcare team performance. METHODS: We present a narrative review that examines how team cognition is assessed in healthcare team simulation, critically consider assessment strategies described in key studies, and contrast them to advances in human factors and organizational psychology. RESULTS: This study presents a framework that reconceptualizes how we measure team cognition in healthcare simulation along the matrices of directness and timing of evaluation. We pair this framework with a table that exemplifies extant measurement techniques and highlight how simulation educators may decide between different 'types' of assessment based upon their needs. DISCUSSION: We offer recommendations for educators to consider capturing team cognition before, during, and after simulation. We also offer recommendations for researchers to develop tools that may be more readily applied across key settings. CONCLUSION: Here, we present a framework of team cognition for healthcare action teams that advances healthcare simulation to better align with human factors and organizational psychology literature. This work will guide healthcare simulation educators and researchers on their quest to optimize team performance through improved team cognition. TRIAL REGISTRATION: None.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it