The physician as person framework: How human nature impacts empathy, depression, burnout, and the practice of medicine
Bibliographic record
Abstract
Troubling trends of depression, burnout, and declines in empathy have been demonstrated amongst residents. I argue that while interventions in medical education are helpful, a new perspective on the issue requires a more fundamental understanding of this problem. Rather than training physicians to act in certain ways, we must first recognize that physicians are first and foremost people. This core principle forms the basis of the framework that educators can use to help learners. Five areas of humanity with implications for physicians are discussed: 1) Physicians and patients share their humanity; 2) People are self-integrated in both personal and professional lives; 3) People are dynamic, thoughtful, and emotional; 4) People are finite; and 5) People are moral beings. Recognizing these can mitigate various factors contributing to current struggles. I also discuss practical implications of this framework to help residents flourish.
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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 | 0.003 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".