Decline of Empathy During Medical Education
Bibliographic record
Abstract
To the Editor: Physician empathy is crucial for strengthening the physician–patient relationship and improving patients’ satisfaction and comfort. However, Hojat and colleagues’ excellent cross-sectional study1 reports a significant decline in empathy among DO-degree medical students in the United States from the preclinical to clinical phases of education. Their current findings are similar to those of their previous landmark cohort study of U.S. MD-degree medical students.2 Outside the United States, while several cross-sectional studies have been conducted, multi-institutional cohort studies to determine the causal factors of this decline have not yet been undertaken. We propose an international multi-institutional prospective cohort study to pinpoint these factors. Its findings could help mitigate this decline among students worldwide, many of whom will go on to become international medical graduates (IMGs) that practice in the United States. Nearly a quarter of resident physicians in the United States are IMGs, with approximately 20% of these having attended medical schools in India.3 A similar decline of empathy is reported from India as well, where levels of empathy among students were, notably, lower than their Western peers.4 Another cross-sectional study in Brazil5 found that trainees’ empathy correlated with burnout levels. Thus, students’ empathy needs preservation universally, so that they deliver the best possible patient care worldwide. Through a multi-institutional cross-sectional study across India, analyses ongoing, we have also investigated this in over 2,000 medical students. To our knowledge, it is the largest study of empathy in medical students in the developing world. We, like Hojat and colleagues,1,2 found a significant longitudinal decline in students’ empathy. We found that certain sociodemographic, cultural, and academic characteristics were significant covariates of this decline. However, these works, due to their cross-sectional nature, are insufficient for determining the causal factors of this decline. Thus, we need multi-institutional prospective cohort studies to determine these factors.
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.
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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".