The art of medicine: arts-based training in observation and mindfulness for fostering the empathic response in medical residents
Bibliographic record
Abstract
Empathy is an essential attribute for medical professionals. Yet, evidence indicates that medical learners' empathy levels decline dramatically during medical school. Training in evidence-based observation and mindfulness has the potential to bolster the acquisition and demonstration of empathic behaviours for medical learners. In this prospective cohort study, we explore the impact of a course in arts-based visual literacy and mindfulness practice ( Art of Seeing ) on the empathic response of medical residents engaged in obstetrics and gynaecology and family medicine training. Following this multifaceted arts-based programme that integrates the facilitated viewing of art and dance, art-making, and mindfulness-based practices into a practitioner-patient context, 15 resident trainees completed the previously validated Interpersonal Reactivity Index, Compassion, and Mindfulness Scales. Fourteen participants also participated in semistructured interviews that probed their perceived impacts of the programme on their empathic clinical practice. The results indicated that programme participants improved in the Mindfulness Scale domains related to self-confidence and communication relative to a group of control participants following the arts-based programme. However, the majority of the psychometric measures did not reveal differences between groups over the duration of the programme. Importantly, thematic qualitative analysis of the interview data revealed that the programme had a positive impact on the participants' perceived empathy towards colleagues and patients and on the perception of personal and professional well-being. The study concludes that a multifaceted arts-based curriculum focusing on evidence-based observation and mindfulness is a useful tool in bolstering the empathic response, improving communication, and fostering professional well-being among medical residents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".