Work-Life Imbalance: A Challenge and an Opportunity for Neurosurgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Work-life balance (WLB) is the individual's view that personal and professional activities in their life align with current life priorities. WLB is important for health and is thought to prevent burnout in the workplace. Although high rates of burnout exist in neurosurgery (NS), studies of WLB and the factors that influence WLB in NS are not known. METHODS: An electronic international survey using a physician wellness framework was conducted globally. χ 2 tests were used to analyze the association between WLB and age, sex, level of practice, and continent of practice. RESULTS: Of 446 respondents (65% staff, 35% trainees; median age range 35-44 years age category; 28% women), only 42% indicated the presence WLB. The presence of WLB was significantly lower in trainees compared with staff (χ 2 = 14.065, P = .0002, odds ratio [OR]: 0.45 [95% CI: 0.30-0.68]), those aged 44 years and below (χ 2 = 4.1464, P = .04172; OR: 0.63 [95% CI: 0.41-0.96]), and those in the African region compared with non-African region (χ 2 = 8.33, P = .0039, OR: 0.42 [95% CI: 0.24-0.75]). CONCLUSION: Nearly two-thirds of those in NS report poor WLB with trainees and younger individuals at particular risk. Lack of sufficient numbers of neurosurgeons for the workload and the lack of support staff require urgent attention globally. There is an urgent need for healthcare organizations globally to take leadership in implementing practices to improve WLB. Evidence shows these changes will likely improve personal and organizational well-being, retention, and improve medical student interest in NS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".