Burnout in Pediatric Emergency Medicine Physicians
Bibliographic record
Abstract
OBJECTIVES: This study aims to determine the prevalence of and identify predictors associated with burnout in pediatric emergency medicine (PEM) physicians and to construct a predictive model for burnout in this population to stratify risk. METHODS: We conducted a cross-sectional electronic survey study among a random sample of board-certified or board-eligible PEM physicians throughout the United States and Canada. Our primary outcome was burnout assessed using the Maslach Burnout Inventory on 3 subscales: emotional exhaustion, depersonalization, and personal accomplishment. We defined burnout as scoring in the high-degree range on any 1 of the 3 subscales. The Maslach Burnout Inventory was followed by questions on personal demographics and work environment. We compared PEM physicians with and without burnout using multivariable logistic regression. RESULTS: We studied a total of 416 PEM board-certified/eligible physicians (61.3% women; mean age, 45.3 ± 8.8 years). Surveys were initiated by 445 of 749 survey recipients (59.4% response rate). Burnout prevalence measured 49.5% (206/416) in the study cohort, with 34.9% (145/416) of participants scoring in the high-degree range for emotional exhaustion, 33.9% (141/416) for depersonalization, and 20% (83/416) for personal accomplishment. A multivariable model identified 6 independent predictors associated with burnout: 1) lack of appreciation from patients, 2) lack of appreciation from supervisors, 3) perception of an unfair clinical work schedule, 4) dissatisfaction with promotion opportunities, 5) feeling that the electronic medical record detracts from patient care, and 6) working in a nonacademic setting (area under the receiver operating characteristic curve, 0.77). A predictive model demonstrated that physicians with 5 or 6 predictors had an 81% probability of having burnout, whereas those with zero predictors had a 28% probability of burnout. CONCLUSIONS: Burnout is prevalent in PEM physicians. We identified 6 independent predictors for burnout and constructed a scoring system that stratifies probability of burnout. This predictive model may be used to guide organizational strategies that mitigate burnout and improve physician well-being.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".