A Comprehensive Umbrella Review for Understanding Burnout in Orthopaedic Surgery
Bibliographic record
Abstract
Background: Burnout is a work-related syndrome characterized by depersonalization, emotional exhaustion, and low personal achievement. Occupational demands can lead to burnout in orthopaedic surgeons, negatively impacting patients and surgeons alike. Several systematic reviews have summarized the literature on specific aspects of burnout in orthopaedic surgery, or on orthopaedic surgeons as a subgroup in their analyses, but a high-level overview of this field is lacking. Aims: We, therefore, aimed to conduct an umbrella review (i.e., review of reviews) summarizing evidence on burnout in orthopedic surgery, focusing on its rates, associations, prevention, and management. Methods: We searched Ovid Medline, Ovid PsycINFO, EBSCO CINAHL, and CENTRAL from database inception to 16 July 2022, using the terms “orthopedic surgery”, “burnout”, and “review”. Quality assessments were conducted using the PASS checklist. An article was considered for inclusion if it was a review, summarized evidence on burnout in orthopedic surgery, and had a full text available to allow for data extraction. We present the results of our review using narrative syntheses. Results: We included eight systematic reviews and eight narrative reviews. We found burnout to be common among orthopedic surgeons, although reviews reported variable rates, thereby precluding definitive conclusions. Residents were found to be particularly at risk of experiencing burnout, with estimates of one in two being affected. Burnout was found to be positively associated with several personal-related factors (including identifying as a female or as a racial minority, experiencing work-life imbalances, and not having spousal support), as well as work-related factors (including working long hours, having stressful work relationships, and experiencing anxiety about one’s clinical competence). Literature on interventions for preventing and managing burnout was limited, although there was some evidence supporting work-hour restrictions for residents. Conclusions: Although burnout is detrimental for orthopaedic surgeons and their patients, high-quality literature in this field is scarce. Future efforts should be dedicated to conducting large-scale, prospective studies to examine burnout rates and associations as well as interventional studies to prevent and manage burnout. We provide recommendations to guide such efforts in our complementary paper on system-level interventions for burnout in orthopedic surgery.
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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 | 0.030 | 0.099 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.058 | 0.042 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".