Interprofessional team debriefings with or without an instructor after a simulated crisis scenario: An exploratory case study
Bibliographic record
Abstract
The value of debriefing after an interprofessional simulated crisis is widely recognised; however, little is known about the content of debriefings and topics that prompt reflection. This study aimed to describe the content and topics that facilitate reflection among learners in two types of interprofessional team debriefings (with or without an instructor) following simulated practice. Interprofessional operating room (OR) teams (one anaesthesia trainee, one surgical trainee, and one staff circulating OR nurse) managed a simulated crisis scenario and were randomised to one of two debriefing groups. Within-team groups used low-level facilitation (i.e., no instructor but a one-page debriefing form based on the Ottawa Global Rating Scale). The instructor-led group used high-level facilitation (i.e., gold standard instructor-led debriefing). All debriefings were recorded, transcribed, and thematically analysed using the inductive qualitative methodology. Thirty-seven interprofessional team-debriefing sessions were included in the analysis. Regardless of group allocation (within-team or instructor-led), the debriefings centred on targeted crisis resource management (CRM) content (i.e., communication, leadership, situation awareness, roles, and responsibilities). In both types of debriefings, three themes emerged as topics for entry points into reflection: (1) the process of the debriefing itself, (2) experience of the simulation model, including simulation fidelity, and (3) perceived performance, including the assessment of CRM. Either with or without an instructor, interprofessional teams focused their debriefing discussion on targeted CRM content. We report topics that allowed learners to enter reflection. This is important for understanding how to maximise learning opportunities when creating education activities for healthcare providers that work in interprofessional settings.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".