Wellness and burnout in cardiac surgery: not black and white
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite a growing emphasis on burnout in medicine, there remains a paucity of data in cardiac surgery. Herein, we summarize recent data on cardiac surgeon well being and identify factors for consideration in future burnout inquiries and management. RECENT FINDINGS: Overall, 70-90% of cardiothoracic surgeons report job satisfaction in the United States. However, 35-60% still endorse burnout symptoms, and the specialty reports some of the highest rates of depression (35-40%) and suicidal ideation (7%). Such negative experiences are greater among early-stage and female surgeons and may be addressed through targeted, program-specific wellness policies. Canada's single-payer healthcare system might exacerbate surgeon burnout due to lower financial compensation and job autonomy. SUMMARY: Cardiothoracic surgeons appear simultaneously burnt out and professionally fulfilled. They report a high incidence of depression and clock in the most hours, yet the majority would choose this specialty again. These findings reveal a more nuanced state of well being than previously appreciated and speak to ambiguities in how burnout is conceived and measured. A broader examination across surgical and social contexts highlights the hierarchical nature of burnout factors and potential ways forward. Collectively, these insights can inform assessments of burnout in Canadian cardiac surgery that remain absent to date.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".