Addressing burnout in surgery and vascular surgery
Bibliographic record
Abstract
Objective By exploring the scientific literature, this article seeks to equip healthcare professionals with the knowledge to identify solutions to burnout within surgery and vascular surgery. Methods A narrative literature review included French and English articles and was conducted in April 2023 with the help of PubMed and Google Scholar databases. Our search included specific MeSH (Medical Subject Heading) terms such as "burnout," "solution," and "healthcare." The review focused on surgical specialties, with a particular lens toward vascular surgery when evidence was available. However, it was broadened to include non-surgical specialties to address knowledge gaps. Through the literature review, we canvased information about operational interventions against burnout, which was then described descriptively. Results We presented a summary of interventions to mitigate burnout as a tiered-approach, categorized into three groups that encompass the individual, the team, and the system. Research supports individual-focused interventions that enhance work-life balance and the use of other tools such as peer support groups, coaching, and counseling. Team-based strategies encompass relationships and mentorship as vital positive factors that curb burnout rates. Finally, the literature advocates for organizational support through good leadership and institutional investment into the workforce's culture and well-being for solutions to burnout at the system level. Conclusions The prevalence of burnout in healthcare professionals is a public health crisis. Indeed, contemporary evaluations in the vascular surgery specialty demonstrate that nearly half of the workforce has experienced burnout. This paper explores the current literature to identify solutions that could help address burnout for vascular surgeons. Current literature supports a tiered approach to mitigate burnout that encompasses elements at the individual, team, and organizational levels.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".