“I don’t know if I can keep doing this”: a qualitative investigation of surgeon burnout and opportunities for organization-level improvement
Bibliographic record
Abstract
Introduction: Burnout is a pressing issue within surgical environments, bearing considerable consequences for both patients and surgeons alike. Given its prevalence and the unique contextual factors within academic surgical departments, it is critical that efforts are dedicated to understanding this issue. Moreover, active involvement of surgeons in these investigations is critical to ensure viability and uptake of potential strategies in their local setting. Thus, the purpose of this study was to explore surgeons' experiences with burnout and identify strategies to mitigate its drivers at the level of the organization. Methods: A qualitative case study was conducted by recruiting surgeons for participation in a cross-sectional survey and semi-structured interviews. Data collected were analyzed using reflexive thematic analysis, which was informed by the Areas of Worklife Model. Results: Overall, 28 unique surgeons participated in this study; 11 surgeons participated in interviews and 22 provided responses through the survey. Significant contributors to burnout identified included difficulties providing adequate care to patients due to limited resources and time available in academic medical centers and the moral injury associated with these challenges. The inequitable remuneration associated with education, administration, and leadership roles as a result of the Fee-For-Service model, as well as issues of gender inequity and the individualistic culture prevalent in surgical specialties were also described as contributing factors. Participants suggested increasing engagement between hospital leadership and staff to ensure surgeons are able to access resources to care for their patients, reforming payment plans and workplace polities to address issues of inequity, and improving workplace social dynamics as strategies for addressing burnout. Discussion: The high prevalence and negative sequalae of burnout in surgery necessitates the formation of targeted interventions to address this issue. A collaborative approach to developing interventions to improve burnout among surgeons may lead to feasible and sustainable solutions.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".