An exploration of changes in cognitive and emotional empathy among medical students in the Caribbean
Bibliographic record
Abstract
OBJECTIVE: This study explored the empathy profile of students across five years of medical training. In addition the study examined whether the Jefferson Scale for Physician Empathy correlated with a measure of cognitive empathy, the Reading the Mind in the Eyes Test and a measure of affective empathy, the Toronto Empathy Questionnaire. METHODS: The study was a comparative cross-sectional design at one Caribbean medical school. Students were contacted in class, participation was voluntary and empathy was assessed using all three instruments Descriptive statistics were calculated and differences between groups evaluated using non-parametric tests. RESULTS: Overall 669 students participated (response rate, 67%). There was a significant correlation between the Jefferson Scale of Physician Empathy and the Toronto Empathy Questionnaire (ρ = 0.48), both scales indicating a decline in medical student empathy scores over time. There was, however, little correlation between scores from the Reading the Mind in the Eyes Test and the Jefferson Scale of Physician Empathy. Female students demonstrated significantly higher scores on all three measures. CONCLUSIONS: Medical students' lower empathy scores during their final years of training appear to be due to a change in the affective component of empathy. These findings may reflect an adaptive neurobiological response to the stressors associated with encountering new clinical situations. Attention should be paid not only to providing empathy training for students but also to teaching strategies for improved cognitive processing capacity when they are encountering new and challenging circumstances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".