Playing well with others: lessons from theatre for the health professions about collaboration, creativity and community
Bibliographic record
Abstract
Despite collaboration among different professions being recognized as fundamentally important to contemporary and future healthcare practice, the concept is woefully undertheorized. This has implications for how health professions educators might best introduce students to interprofessional collaboration and support their transition into interprofessional, collaborative workplaces. To address this, we engage in a conceptual analysis of published collaborative, interprofessional practices and conceptual understandings in theatre, as a highly collaborative art form and industry, to advance thinking in the health professions, specifically to inform interprofessional education. Our analysis advances a conceptualization of collaboration that takes place within a work culture of creativity and community, that includes four modes of collaboration, or the ways theatre practitioners collaborate, by: (1) paying attention to and traversing roles and hierarchies; (2) engaging in reciprocal listening and challenging of others; (3) developing trust and communication, and; (4) navigating uncertainty, risk and failure. We conclude by inviting those working in the health professions to consider what might be gleaned from our conceptualization, where the embodied and human-centred aspects of working together are attended to alongside structural and organizational aspects.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.075 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".