Perioperative inter-professional education training enhance team performance and readiness
Bibliographic record
Abstract
Background Nursing students often experience anxiety during their transition to real healthcare environments, primarily due to a lack of training with professionals from other specialties. We developed an interprofessional education (IPE) course for nursing students and surgical residents to refine their perioperative skills in a simulation environment. We quantified the impact of this IPE course on students' team performance. Methods Fifteen health participants, comprising five surgical residents and 10 nursing students, were organized into 10 interprofessional surgical teams. Each interdisciplinary team performed two open cholecystectomies in simulation, with a brief debriefing phase in between. Team performance and participants' perceptions of IPE training were surveyed. Video analysis identified collaborative behaviors, including anticipatory movements. Results Team performance score showed a significant improvement on the second trial, particularly among nursing students. Participants improved their attitudes and readiness regarding the IPE program. Interestingly, nursing students exhibited more anticipatory movements during the second trial, a behavioral improvement not observed in surgical residents. Conclusion Perioperative IPE training produce more pronounced improvement observed among nursing students after the debriefing phase.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".