Survey Based Assessment of Burnout Rates Among US Plastic Surgery Residents
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to analyze the rates of burnout and contributory factors among US plastic surgery residents. METHODS: The Maslach Burnout Inventory Human Services Survey was emailed to program coordinators of American College of Graduate Medical Education-accredited plastic surgery residencies. Scores are provided for 3 subscales: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment. Normative scoring tables (low, average, high) were used for comparison. Residents were asked questions relating to their personal life (age, postgraduate year, marital status, and program characteristics). RESULTS: One hundred thirteen residents responded. The average age was 31.6 years (range, 25-43 years) and postgraduate year of 4.6 (range, 1-10). There were equal male and female respondents. Most were from integrated-only residencies (n = 59, 52.2%). On average, the majority reported working 50 to 80 hours per week (n = 93, 82.3%), spending the majority of time in tertiary referral centers (n = 107, 94.7%). Most received and took 3 weeks of vacation per year (n = 68, 60.7%). Furthermore, 65.5% met the definition of burnout by their scores from at least 1 subscale.The number of hours worked per week significantly correlated with increased scores in the EE and DP subscales. Residents who worked more than 80 hours per week had significantly higher scores in the EE and DP categories. Residents who had less than 2 weeks of vacation per year trended toward experiencing more EE (EE; 46.0, P = 0.077). The type of program (independent vs integrated), sex, having a significant other outside of the home, kids, and local family support did not significantly affect burnout scores for any subscales. CONCLUSIONS: Burnout exists among plastic surgery residents especially in the DP subscale. Working longer hours and less vacation correlates with increased rates of burnout among residents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".