End-of-Life Education Using the Dramatic Arts: The Wit Educational Initiative
Bibliographic record
Abstract
Caring for dying persons requires skill in interpersonal aspects of care, which may be difficult to teach using conventional educational methods. The Pulitzer Prize-winning play Wit relates the personal story of a patient dying from metastatic ovarian cancer and describes the protagonist's experience with medical care from diagnosis to death. Members of the Department of Medicine at the VA Greater Los Angeles Health care System and the David Geffen School of Medicine, UCLA developed a program that utilized Wit to educate medical students, residents, and staff providers in the humanistic elements of end-of-life care. Between February 2000 and January 2002 the Wit Educational Initiative organized on-site readings of Wit by local professional theatre companies at medical centers throughout the United States and Canada, inviting medical students, housestaff, and other providers to attend the play followed by structured discussions of the play's themes. The Initiative provided extensive support for potential program sites including publicity, providing a handbook with a step-by-step guide to organizing local programs, and feedback of postperformance survey results. The Initiative was successful in organizing performances at 32 out of 54 (59%) medical centers where a local production of Wit was identified. Survey respondents confirmed the appeal, emotional impact, and perceived relevance of drama in end-of-life education. An educational program using theatre to educate trainees in the humanistic aspects of end-of-life care was enthusiastically received by medical schools and rated highly by attendees.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".