Palliative care and the arts: vehicles to introduce medical students to patient-centred decision-making and the art of caring
Bibliographic record
Abstract
BACKGROUND: Medical Schools are challenged to improve palliative care education and to find ways to introduce and nurture attitudes and behaviours such as empathy, patient-centred care and wholistic care. This paper describes the curriculum and evaluation results of a unique course centred on palliative care decision-making but aimed at introducing these other important competencies as well. METHODS: The 20 h-long optional course, presented in an art museum, combined different learning methods, including reflections on art, case studies, didactic sessions, personal experiences of faculty, reflective trigger videos and group discussions. A mixed methods approach was used to evaluate the course, including a) a post-course reflective exercise; b) a standardized evaluation form used by the University for all courses; and c) a focus group. RESULTS: Twenty students (2nd to 6th years) participated. The course was rated highly by the students. Their understanding of palliative care changed and misconceptions were dispelled. They came to appreciate the multifaceted nature of decision-making in the palliative care setting and the need to individualize care plans. Moreover, the course resulted in a re-conceptualization of relationships with patients and families, as well as their role as future physicians. CONCLUSIONS: Palliative care decision-making therefore, augmented by the visual arts, can serve as a vehicle to address several competencies, including the introduction of competencies related to being patient-centred and empathic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".