Burnout in Diplomates of the American Board of Physical Medicine and Rehabilitation—Prevalence and Potential Drivers: A Prospective Cross‐Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Physician burnout is of growing concern. Burnout among physical medicine and rehabilitation (PM&R) physicians has shown a significant increase, positioning PM&R as one of the most "burned out" of specialties. Little has been written about potential factors contributing to physiatrist burnout or potential interventions. OBJECTIVE: To determine the prevalence of burnout among physiatrists and identify risk factors for burnout and potential strategies to decrease burnout among physiatrists. DESIGN: Prospective cross-sectional survey. SETTING: National survey of board certified physiatrists. PARTICIPANTS: One thousand five hundred thirty-six physiatrists certified by the American Board of PM&R. OUTCOME: The Mini-Z Burnout Survey, 1 question from the Maslach Burnout Scale on callousness toward patients, and several potential drivers of burnout. The probability of burnout, identified by question 3 on the Mini-Z, was the dependent variable. Other questions on the Mini-Z were explored as independent variables using logistic regression. RESULTS: Seven hundred seventy physiatrists (50.7%) fulfilled the definition of burnout. Only 38% of physiatrists reported not becoming more callous toward patients. The top 3 causes of burnout identified by physiatrists were increasing regulatory demands, workload and job demands, and practice inefficiency and lack of resources. Higher burnout rate was associated with high levels of job stress and working more hours per week. Lower burnout rates were associated with higher job satisfaction, control over workload, professional values aligned with those of department leaders, and sufficient time for documentation. There was no significant association between burnout and sex, years in practice, practice focus, or practice area. CONCLUSION: Burnout is a significant problem among PM&R physicians and is pervasive throughout the specialty. Opportunities exist to address major contributing drivers of burnout relating to practice patterns and efficiency of care within PM&R. These opportunities are, to varying degrees, under the control of hospital leaders, practice administrators, and practitioners.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".