ACTup: advanced communication training simulation enhanced by actors trained in the Stanislavski system
Bibliographic record
Abstract
Strong communication, empathy and interpersonal skills are crucial to good clinical practice. Actors trained in interpretations of the Stanislavski system draw on their own life experience to develop the character. We hypothesised that simulation enhanced by trained actors would be an ideal way for our senior trainees to develop advanced communication skills. We developed a communication training course based on challenging situations which occur in paediatrics like child death and safeguarding. Actors were briefed and invited to develop characters that would behave and respond as a parent/carer might do in complex and stressful clinical scenario. Paediatric trainees then participated in simulations, with a focus on communication skills. Feedback and debrief were provided by a multidisciplinary faculty. The impact of the course was evaluated by analysis of data collected in focus groups held after the simulation. Trainees noted the actor's ability to respond in vivo to emotive situations and felt it was much more effective than their previous experience of simulation with simulated patients without formal training. Actors were able to offer feedback on aspects of body language, tone and use of language from a non-medical perspective. Actors enhanced the realism of the simulations by changing their language and emotional performance in response to the trainee's performance, improving trainee engagement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".