Parenthood and neurosurgery in Europe, a white paper from the European association of neurosurgical societies’ diversity in neurosurgery committee, part II – practice with children
Bibliographic record
Abstract
Introduction: In the first part of this White Paper, the European Association of Neurosurgical Societies (EANS) Diversity in Neurosurgery Committee (DC) addressed the obstacles faced by neurosurgeons when planning to have a family and practice during pregnancy, attempting to enumerate potential, easily implementable solutions for departments to be more family-friendly and retain as well as foster talent of parent-neurosurgeons, regardless of their gender identity and/or sexual orientation. Attrition avoidance amongst parent-neurosurgeons is at the heart of these papers. Research question: In this second part, we address the obstacles posed by practice with children and measures to mitigate attrition rates among parent-neurosurgeons. For the methodology employed to compose this White Paper, please refer to Supplementary Electronic Materials (SEM) 1. Materials and methods: For composing these white papers, the European Association of Neurosurgical Societies (EANS)'s Diversity Committee (DC) recruited neurosurgeon volunteers from all member countries, including parents, aspiring parents, and individuals without any desire to have a family to create a diverse and representative working group (WG). Results: In spite of the prevailing heterogeneity in policies across the continent, common difficulties can be identified for both mothers and fathers considering the utilization of parental leave. Discussion and conclusion: Reconciliation of family and a neurosurgical career is challenging, especially for single parents. However, institutional support in form of childcare facilities and/or providers, guaranteed lactation breaks and rooms, flexible schedule models including telemedicine, and clear communication of policies can improve working conditions for parent-neurosurgeons, avoid their attrition, and foster family-friendly work environments.
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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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".