The art of empathy: Teaching empathy through art
Bibliographic record
Abstract
BACKGROUND: The instruction of empathy is challenging. Although several studies have addressed how art-based education can foster empathy, there is a need for more evidence showing its impact and students' perceptions, especially in graduate education. APPROACH: We designed and implemented a virtual art-based curriculum focused on fostering empathy-The Art of Empathy. This novel curriculum used diverse art-based education methodologies to promote meticulous and collaborative observation and reflection, building on constructivism. Thirty-six interns at the Brigham and Women's Hospital were invited to participate in the curriculum, while 34 served as control. EVALUATION: We used mixed methods to explore interns' perceptions of the curriculum and assess its impact on their empathy. We used two quantitative instruments with known psychometric characteristics: the Toronto Empathy Questionnaire (TEQ) and the Jefferson Scale of Physicians Empathy (JSPE), which were distributed in a survey and completed by 31/99 (31.3%). We collected qualitative data from four interns using semi-structured interviews. Thematic analysis showed how The Art of Empathy promoted interns' reflections and actions toward empathy. This was partially supported by the quantitative data that showed significantly higher scores on the 'Compassionate Care' subscale of the JSPE (p = 0.039) when compared with interns in the control group. The thematic analysis showed that interns appreciated the curriculum and valued its benefits while highlighting the limitations of the virtual delivery approach. IMPLICATIONS: Our curriculum was well received by interns and showed the potential of art-based methodology to promote empathic capacities in graduate students.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".