Empathy in medical education and practice
Bibliographic record
Abstract
Background: Empathy is a cornerstone of effective medical practice, encompassing cognitive, emotional, moral, and behavioral dimensions. Despite its profound impact on patient outcomes and physician well-being, declines in empathy during medical training remain a concern. This study aimed to assess empathy levels among medical students across different training years and explore factors influencing these levels. Methodology: A cross-sectional survey was conducted among 409 medical and dental students in Andhra Pradesh and Telangana. Empathy levels were assessed using the Toronto Empathy Questionnaire, a validated 16-item self-report tool, based on which the participants were categorized as having “below-average empathy” or “good empathy.” Data analysis was performed using the SPSS Version 20.0, with descriptive and analytical statistics examining associations with demographic and academic factors. Results: Of the participants, 50.9% were having below-average empathy. Empathy levels varied across academic years, with 2 nd -year students demonstrating the highest proportion of “good empathy” (53.5%), though differences across years were not statistically significant ( P = 0.78). Similarly, no significant differences were found between MBBS and BDS students ( P = 0.55). Gender was significantly associated with empathy levels, with females exhibiting higher empathy scores ( P = 0.001). Specialty preferences did not significantly correlate with empathy levels ( P = 0.64). Conclusion: While empathy is critical for healthcare professionals, its variability across genders and the lack of a consistent trend across academic years call for innovative educational strategies. Incorporating empathy-focused training into medical curricula could serve as an effective method for nurturing more compassionate and patient-centered future healthcare providers.
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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 | 0.004 | 0.013 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".