Trends in Surgeon Burnout in the US and Canada: Systematic Review and Meta-Regression Analysis
Bibliographic record
Abstract
BACKGROUND: Burnout among surgeons is increasingly recognized as a crisis. However, little is known about changes in burnout prevalence over time. We evaluated temporal trends in burnout among surgeons and surgical trainees of all specialties in the US and Canada. STUDY DESIGN: We systematically reviewed MEDLINE, Embase, and PsycINFO for studies assessing surgeon burnout from January 1981 through September 2021. Changes in dichotomized Maslach Burnout Inventory scores and mean subscale scores over time were assessed using multivariable random-effects meta-regression. RESULTS: Of 3,575 studies screened, 103 studies representing 63,587 individuals met inclusion criteria. Publication dates ranged from 1996 through 2021. Overall, 41% of surgeons met criteria for burnout. Trainees were more affected than attending surgeons (46% vs 36%, p = 0.012). Prevalence remained stable over the study period (-4.8% per decade, 95% CI -13.2% to 3.5%). Mean scores for emotional exhaustion declined and depersonalization declined over time (-4.1 per decade, 95% CI -7.4 to -0.8 and -1.4 per decade, 95% CI -3.0 to -0.2). Personal accomplishment scores remained unchanged. A high degree of heterogeneity was noted in all analyses despite adjustment for training status, specialty, practice setting, and study quality. CONCLUSIONS: Contrary to popular perceptions, we found no evidence of rising surgeon burnout in published literature. Rather, emotional exhaustion and depersonalization may be decreasing. Nonetheless, burnout levels remain unacceptably high, indicating a need for meaningful interventions across training levels and specialties. Future research should be deliberately designed to support longitudinal integration through prospective meta-regression to facilitate monitoring of trends in surgeon burnout.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".