Off-Script, Online: Virtual Medical Improv Pilot Program for Enhancing Well-being and Clinical Skills among Psychiatry Residents
Bibliographic record
Abstract
Objective: Clinical interactions demand a balance of structure and flexibility in response to unpredictable situations. Medical improv is a form of experiential learning that applies techniques from improvisational theater to the healthcare setting, deliberately targeting clinical skills of communication, teamwork, and cognitive abilities. Psychiatry Education through Play and Talk (PEP Talks) is a novel medical improv program designed specifically for psychiatry residents with the goal of improving communication, teamwork, and conflict resolution skills, as well as enhancing residents' well-being and capacity for self-reflection. Methods: PEP Talks was delivered virtually by an experienced medical improv facilitator in spring 2021 to a self-selected group of psychiatry residents at a Canadian university. Aligned with the context-input-process-product (CIPP) evaluation model, outcomes were assessed through mixed methods surveys, recorded debriefings, and a focus group. Results: PEP Talks enhanced residents' self-reported well-being, reflective capacity, and communication skills. Participants made qualitative connections between PEP Talks and their well-being, inter- and intra-personal skills, and clinical experiences in psychiatry. Processes in PEP Talks that led to these outcomes included the following: joy, building community, personal reflection and discovery, going off-script, immersion, and virtual engagement. Conclusions: Virtual medical improv offers an innovative solution to the pedagogical challenges of training psychiatrists to be proficient communicators, collaborators, and professionals capable of reflective practice. Additionally, this innovation demonstrates that medical improv can be delivered in a virtual format and may offer a unique solution to support resident well-being and foster connection amid remote learning during a global pandemic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".