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 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".