Association Between Empathy and Burnout Among Emergency Medicine Physicians
Bibliographic record
Abstract
BACKGROUND: The association between physician self-reported empathy and burnout has been studied in the past with diverse findings. We aimed to determine the association between empathy and burnout among United States emergency medicine (EM) physicians using a novel combination of tools for validation. METHODS: This was a prospective single-center observational study. Data were collected from EM physicians. From December 1, 2018 to January 31, 2019, we used the Jefferson scale of empathy (JSE) to assess physician empathy and the Copenhagen burnout inventory (CBI) to assess burnout. We divided EM physicians into different groups (residents in each year of training, junior/senior attendings). Empathy, burnout scores and their association were analyzed and compared among these groups. RESULTS: A total of 33 attending physicians and 35 EM residents participated in this study. Median self-reported empathy scores were 113 (interquartile range (IQR): 105 - 117) in post-graduate year (PGY)-1, 112 (90 - 115) in PGY-2, 106 (93 - 118) in PGY-3 EM residents, 112 (105 - 116) in junior and 114 (101 - 125) in senior attending physicians. Overall burnout scores were 43 (33 - 50) in PGY-1, 51 (29 - 56) in PGY-2, 43 (42 - 53) in PGY-3 EM residents, 33 (24 - 47) in junior attending and 25 (22 - 53) in senior attending physicians separately. The Spearman correlation (ρ) was -0.11 and β-weight was -0.23 between empathy and patient-related burnout scores. CONCLUSION: Self-reported empathy declines over the course of EM residency training and improves after graduation. Overall high burnout occurs among EM residents and improves after graduation. Our analysis showed a weak negative correlation between self-reported empathy and patient-related burnout among EM physicians.
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How this classification was reachedexpand
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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".