“There’s no billing code for empathy” - Animated comics remind medical students of empathy: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Physician empathy is associated with improved diabetes outcomes. However, empathy declines throughout medical school training. This study seeks to describe how comics on diabetes affect learning processes for empathy in medical students. METHODS: All first- or second-year students at a Canadian medical school were invited to provide written reflections on two comics regarding diabetes and participate in a focus group. Responses were analyzed qualitatively for emergent themes. Students completed the Jefferson Scale of Physician Empathy (JSPE) at baseline, after the comic, and after the focus group. Linear mixed model statistical analyses were performed. RESULTS: Thirteen first-year and 12 second-year students participated. Qualitative analysis revealed four themes: 1) Empathy decline and its barriers; 2) Impact of the comic and focus group on knowledge, attitudes and skills; 3) Role of the comic in the curriculum as a reminder tool of the importance of empathy; 4) Comics as an effective medium. Baseline mean JSPE scores were 116.4 (SD 10.5) and trended up to 117.2 (SD 12.5) and 119.6 (SD 15.2) after viewing the comics and participating in the focus groups, respectively (p = 0.08). CONCLUSIONS: Animated comics on diabetes are novel methods of reminding students about empathy by highlighting the patient perspective.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".