Cognitive versus Technical Debriefing after Simulation Training
Bibliographic record
Abstract
BACKGROUND: Recent literature describes "cognitive dispositions to respond" (CDRs) that may lead physicians to err in their clinical reasoning. OBJECTIVES: To assess learner perception of high-fidelity mannequin-based simulation and debriefing to improve understanding of CDRs. METHODS: Emergency medicine (EM) residents were exposed to two simulations designed to bring out the CDR concept known as "vertical line failure." Residents were then block-randomized to a technical/knowledge debriefing covering the medical subject matter or a CDR debriefing covering vertical line failure. They then completed a written survey and were interviewed by an ethnographer. Four investigators blinded to group assignment reviewed the interview transcripts and coded the comments. The comments were qualitatively analyzed and those upon which three out of four raters agreed were quantified. A random sample of 84 comments was assessed for interrater reliability using a kappa statistic. RESULTS: Sixty-two residents from two EM residencies participated. Survey results were compared by technical (group A, n = 32) or cognitive (group B, n = 30) debriefing. There were 255 group A and 176 group B comments quantified. The kappa statistic for coding the interview comments was 0.42. The CDR debriefing group made more, and qualitatively richer, comments regarding CDR concepts. The technical debriefing group made more comments on the medical subjects of cases. Both groups showed an appreciation for the risk of diagnostic error. CONCLUSIONS: Survey data indicate that technical debriefing was better received than cognitive debriefing. The authors theorize that an understanding of CDRs can be facilitated through simulation training based on the analysis of interview comments.
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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 | 0.035 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".