Medical Students using Theatre to Engage Seniors in Long-Term Care Facilities: Fostering Empathy Through a Humanities Pilot Project
Bibliographic record
Abstract
Background: The implementation of humanities, and particularly theatre, into the medical curriculum is a nascent but promising field. Here, we report on the Theatre in Community Health Project (TCHP), an initiative devised by University of Ottawa medical students focused on the use of performative theatre with residents in a long-term care facility. We also describe the impact on medical students’ developing communication skills and empathy after they complete the TCHP. Methods: Two cohorts of first year medical students at the University of Ottawa participated in the TCHP at Villa Marconi Long Term Care Facility (LTC) over two consecutive years. Medical student participants subsequently each completed a critical reflection of their experience and these were used as the basis of our thematic analysis. Using an inductive thematic analysis, 17 themes and the frequency of statements pertaining to each theme were identified. Results: The analysis of the students’ reflections showed two overarching themes: insight into communicating with geriatric populations and improved insights into long term facilities. Conclusion: Our study of the TCHP programshows a relationship between medical students’ experiences with audience-oriented performative theatre and increased capacity for empathy and communication toward the targeted audience. The mechanisms by which this increased capacity takes place may be twofold: first, enhanced awareness of the behavioural components of empathy and communication; and second, deeper appreciation for how each patient’s individual context shapes the clinical encounter.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".