Exploring work-life integration in vascular surgery and surgery
Bibliographic record
Abstract
Introduction There is a growing emphasis on achieving work-life balance (WLB), defined as the balance between work and personal life. However, this concept is gradually shifting towards work-life integration (WLI), which is relevant in the healthcare sector, particularly surgery. We performed a literature review to evaluate factors that contribute to WLI in the field of surgery, with a particular focus on vascular surgery. Methods A literature review of WLI in surgery, specifically vascular surgery, was performed from inception to February 2024. PubMed was searched for papers written in the English and French languages using the MeSH terms "work-life balance" or "work-life integration" in "vascular surgery" or "surgery." Findings were categorized in a tiered framework focused on faculty or staff, trainees (including medical students, residents, or fellows), and people identifying as underrepresented in medicine (URIM). Results Twenty-four articles were identified as relevant for this review. Previous reports have identified a high prevalence of burnout and suicide among the vascular surgery workforce. Collegial support and institutional culture were identified as pivotal in enhancing WLI. Inefficiencies in healthcare delivery, administrative burdens, and a lack of autonomy were recognized as barriers for WLI. Factors specific to gender and parenthood lead to unequal challenges in achieving WLI. Medical trainees' WLI perceptions influenced their specialty choices and risk of burnout. Also, URIM trainees encountered additional obstacles like discrimination and attrition, though some reports indicated a resilience advantage among minority physicians. Conclusion This review has highlighted differences in challenges related to WLI across faculty and staff, trainees, and individuals identifying as URIM and emphasizes the need for systemic and cultural reforms, flexible work arrangements, and greater support for underrepresented groups to foster a healthier work-life ecosystem in healthcare.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".