Team cognition in healthcare simulation: a framework for deliberate measurement
Bibliographic record
Abstract
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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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 | 0.108 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".