Spine Surgeons Burnout and Quality of Life
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional survey study. OBJECTIVE: To evaluate the prevalence of burnout, assess the personal and professional characteristics associated with burnout in spine surgeons and determine their quality of life. SUMMARY OF BACKGROUND DATA: Burnout is a syndrome characterized by emotional exhaustion, depersonalization, and decreased sense of accomplishment that leads to decreased effectiveness at work. To date, there has been a lack of information on the prevalence of burnout among spine surgeons worldwide and the risk factors associated with this condition. METHODS: An electronic survey with members of AO Spine was performed in May 2018. The survey evaluated demographic variables, practice characteristics, burnout, and quality of life. Maslach Burnout Inventory (MBI) and EuroQol 5-dimensions (EQ5D) were used to evaluate burnout and quality of life, respectively. RESULTS: A total of 818 surgeons from 86 countries completed the survey. The prevalence of burnout was 30.6%. In the multiple linear model, emotional fatigue was independently associated with younger age (B = -0.17, CI95% = -0.26 to -0.07, P < 0.0001), and longer working hours per week (B = -2.71, CI95% = -4.34 to -1.07, P = 0.001); depersonalization was independently associated with younger age (B = -0.13, CI95% = -0.19 to -0.07, P < 0.0001), practicing outside Latin America (LA) (B = 0.71, CI95% = 0.41-1.01, P < 0.0001) and currently being a fellow (B = 0.54, CI95% = 0.06-1.02, P = 0.02); and higher scores of personal fulfilment was associated with practicing in LA (B = -1.27, CI95% = -1.69 to -0.85, P < 0.0001). CONCLUSION: Burnout is a common condition among spine surgeons worldwide. There is a significant association between burnout scores and decreased general quality of life. These results highlight the need to develop interventional programs to better identify, prevent, and manage this condition among practicing spine surgeons.Level of Evidence: 4.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".