Teaching Empathy through Movies: Reaching Learners’ Affective Domain in Medical Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We live in an era where outcomes, guidelines, and clinical trials are at the forefront of medical training. However, to care implies having an understanding of the human being and build reflective practitioners impregnated of a humanistic perspective of doctoring. Although technical knowledge and skills can be acquired through training with little reflective process, it is impossible to refine attitudes, acquire virtues, and incorporate values without reflection. Empathy, which is required for a deep understanding of the human condition, could bridge the gap between patient-centered medicine and evidence-based medicine therefore representing a profound therapeutic potential. The challenge is how to teach empathy, an important issue in medical education, hard to teach and to measure. The authors’ broad experience in medical education using movies points out an innovative methodology to promote empathy because it reaches the learners’ affective domain. A description of the cinematic teaching methodology is provided and an extensive list of movie scenes are included so faculty and educators can try it in their own teaching scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it