Bibliographic record
Abstract
Introduction: Clinical practice includes expressing empathy and understanding key features of humanity, such as mortality and illness. The Stanislavski “System” of actor training negotiates a journey from the unconscious via feeling, will and intellect to a proposed supertask. This study explored these areas during collaborative learning amongst undergraduate medical and drama students. Materials and Methods: Each of two interactive sessions involved teams of final year medical students rotating through challenging simulated clinical scenarios, enacted by undergraduate drama students, deploying key techniques from the Stanslavski system of actor training. Team assessment of performance was via a ratified global scoring system and dynamic debriefing techniques. Results: Medical students reported an enhanced immersive experience within simulated clinical scenarios. Drama students reported increased challenge and immersion within their roles. Medical faculty and standardised patients reported positive utility and value for the approach. Clinical team assessment scores increased by 47% (p < 0.05) with this intervention. Discussion: Qualitative and quantitative data demonstrated the merit and utility of such interdisciplinary learning. All students and faculty appreciated the value of the activity and described enhanced learning. Collaborative dynamic debriefing allowed for a continuation of the immersive experience and allowed for an exploration of arenas such as empathy. Conclusions: The deployment of drama students trained in the Stanislavski system significantly enriched medical and drama student experience and performance. Team assessment scores further demonstrated the effectiveness of this approach. Feedback from students, faculty and standardised patients was uniformly positive. The approach facilitated exploration of empathy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".