Revisiting the Pediatric Neurosurgical Workforce: An Update After 15 Years and Analysis of Generational Change
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The landscape of medicine has undergone significant changes with shifting generational values. Studies are increasingly looking into factors such as work-life balance and diversity in neurosurgery, but few have considered these trends in pediatric neurosurgery. This study investigated the changes in the pediatric neurosurgical workforce over the past 15 years and highlighted generational differences in demographics, practice patterns, and values. METHODS: Databases were compiled using neurosurgical societies to identify current pediatric neurosurgeons. A 36-question survey was administered, and responses were collected for a 6-month period ending in February 2023. Responses were compared between age groups (<45 years, 45-54 years, 55+ years). Results from this survey were compared with a previous survey performed in 2008. RESULTS: Four hundred ninety-five pediatric neurosurgeons received the 2023 survey (response rate: 49%, 241/495). 172 pediatric neurosurgeons were identified after excluding 69 respondents whose practice was not >75% pediatric. Compared with the 2008 cohort, the 2023 cohort were more likely to be female ( P < .001), have American Board of Pediatric Neurological Surgery Society certifications ( P < .001), and have complete pediatric fellowships ( P < .001). The 2023 cohort also had lower case volumes ( P < .001), worked fewer hours per week ( P < .001), were more inclined to grow their practice ( P < .001) when compared with the 2008 cohort. Younger neurosurgeons (<45 years) had more frequent call schedules ( P = .03) and were more likely to anticipate retiring by the age of 65 years ( P < .001) compared with neurosurgeons aged older than 55 years. CONCLUSION: These results reveal a generational shift in the pediatric neurosurgical workforce, suggesting how the field may continue to evolve in the coming decades. Understanding these changes is essential for addressing future challenges in the workforce.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".