Factors Associated With Physician Empowerment and Well-being at an Academic Medical Center
Bibliographic record
Abstract
OBJECTIVE: This study reports an institutional approach to rapidly measure burnout and gather physicians' opinions on workplace factors that empower well-being. METHODS: In July 2017, physicians at Vanderbilt University Medical Center were invited to participate in a two-question survey measuring self-reported burnout and providing an opportunity to describe structures that empower well-being. Free-text responses were analyzed and a linear regression model assessed factors associated with well-being. RESULTS: A total of 1135 physicians responded (43.3% response rate) with a mean well-being score of 56 (scale 0 to 100). Higher scores were associated with clinical fellow status (P = 0.002), male sex (P = 0.008), less allocation of time to clinical care (P < 0.001), and not commenting on "leadership" and "autonomy" in the free-text response. CONCLUSIONS: Brief surveys collecting perspectives on well-being can help employers identify high-risk groups and provide a roadmap for institutional change.
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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 | 0.001 | 0.010 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".