Physician Burnout and the Calling to Care for the Dying.
Bibliographic record
Abstract
BACKGROUND: Physician burnout raises concerns over what sustains physicians' career motivations. We assess whether physicians in end-of-life specialties had higher rates of burnout and/or calling to care for the dying. We also examined whether the patient centeredness of the clinical environment was associated with burnout. METHODS: In 2010 to 2011, we conducted a national survey of US physicians from multiple specialties. Primary outcomes were a validated single-item measure of burnout or sense of calling to end-of-life care. Primary predictors of burnout (or calling) included clinical specialty, frequency of encounters with dying patients, and patient centeredness of the clinical environments ("My clinical environment prioritizes the need of the patient over maximizing revenue"). RESULTS: Adjusted response rate among eligible respondents was 62% (1156 of 1878). Nearly a quarter of physicians (23%) experienced burnout, and rates were similar across all specialties. Half of the responding physicians (52%) agreed that they felt called to take care of patients who are dying. Burned-out physicians were more likely to report working in profit-centered clinical environments (multivariate odds ratio [OR] of 1.9; confidence interval [CI]: 1.3-2.8) or experiencing emotional exhaustion when caring for the dying (multivariate OR of 2.1; CI: 1.4-3.0). Physicians who identified their work as a calling were more likely to work in end-of-life specialties, to feel emotionally energized when caring for the dying, and to be religious. CONCLUSION: Physicians from end-of-life specialties not only did not have increased rates of burnout but they were also more likely to report a sense of calling in caring for the dying.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".