Measuring burnout and professional fulfillment among emergency medicine residency program leaders in the United States: a cross-sectional survey study
Bibliographic record
Abstract
OBJECTIVE: Emergency medicine (EM) physicians face high burnout rates, even in academic settings. Research on burnout among EM residency program leaders is limited, despite their role in shaping the training environment and influencing resident well-being. This study aims to measure burnout and professional fulfillment among EM residency program leaders and identify contributing factors. METHODS: A cross-sectional survey using the adapted Stanford Professional Fulfillment Index was conducted in 2023 to assess burnout and professional fulfillment among EM residency program leaders at US programs. The survey, tailored to EM leaders, was distributed to all current EM program directors (PDs) and assistant/associate PDs (APDs) from accredited US programs. Descriptive statistics and odds ratios were used to compare burnout and professional fulfillment across various groups. RESULTS: A total of 112 of 281 PDs (response rate, 39.9%) and 130 of 577 APDs (response rate, 22.5%) participated. Professional fulfillment was reported by 59.8% of PDs and 58.5% of APDs. Burnout was experienced by 42.0% of PDs and 26.9% of APDs. Higher professional fulfillment correlated with alignment with expectations, positive work environments, and perceived appreciation, while burnout was strongly associated with negative impacts on personal health and relationships. Approximately 27.7% of PDs and 23.8% of APDs expressed an intention to leave their current position within 18 months. CONCLUSION: A significant proportion of US EM residency program leaders experience burnout and low professional fulfillment. Addressing well-being in this population has important implications for education and mentorship provided to future physicians in the field.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".