Activating Empathy Through Art in Cancer Communities
Bibliographic record
Abstract
Background: The Aesthetics of Health (AOH) undergraduate visual art studies course at the University of Texas at Austin aimed to enhance art students' awareness of cancer's impact not only medically but also socially, emotionally, financially, and spiritually and to examine how this experience might impact students' artwork, capacity for empathy, and connection to audience. Methods: During the spring 2021 semester, the AOH course instructors employed assorted pedagogical methods, including art, illness narrative, and community engagement, in special sessions led by professors, community practice artists, and cancer experts, respectively, as well as oral storytelling by those with lived experience of cancer (ie, cancer patients, posttreatment survivors, and loved ones). For the course's final project, the 15 student-artists created self-selected media works combining health and activism and displayed them in public spaces, including online. Student-artists took the Toronto Empathy Questionnaire during the first and last weeks of the course and provided feedback. Two group interviews were also held with cancer storytellers following their participation. Results: Student-artists' average score on the Toronto Empathy Questionnaire increased from 52.46 at pretest to 55.38 at posttest. Student-artists and storytelling participants also reported having positive experiences. Conclusions: The AOH course's social practice approach encouraged student-artists to realize new ideas and relationships and modestly increased their capacity for empathy. The AOH framework demonstrates promise for increasing empathy through the arts in other educational, clinical, and artistic institutions. Further research is needed with larger sample sizes to measure the impact of the course and to demonstrate its potential for addressing burnout and moral distress.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".