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Record W4327931504 · doi:10.1055/s-0043-1762109

Unwellness: Skull Base Surgery's Greatest Challenge and Opportunity

2023· article· en· W4327931504 on OpenAlexaff
Janissardhar Skulsampaopol, Yu Ming, Sylvia Shitsama, Melissa Carpino, Jennifer Anderson, Michael D. Cusimano

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2023
Typearticle
Languageen
FieldMedicine
TopicFacial Trauma and Fracture Management
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsSkullBalance (ability)Base (topology)BurnoutWork (physics)MedicineComputer scienceSurgeryEngineeringPhysical medicine and rehabilitationMechanical engineeringMathematicsClinical psychology

Abstract

fetched live from OpenAlex

Introduction: Skull base surgeons (SBS) are likely to have poor work–life balance (WLB) and burnout due to long and complicated surgeries and demanding clinical needs. However, there are no previous papers that primarily explore the wellness of SBS. Objectives: 1. To assess workload, WLB, and job satisfaction among SBS. 2. To determine rate of burnout among SBS. 3. To compare burnout, workload, WLB, and job satisfaction among SBS and non-SBS. Methods: An international survey developed using a physician wellness framework was sent to staff neurosurgeons (SBS and non-SBS) between June and November 2021. Results: A total of 299 respondents who answered questions regarding their subspecialty were included in the analysis. Forty-seven were SBS and 252 were non-SBS. Over 70% of SBS reported workloads that were too heavy; 63.8% did not have adequate time for their family, and 59.6% were not content with the balance between their work and home life. Almost 50% of SBS stated that they were sleep deprived, had difficulty balancing their professional and personal life, and did not have enough time to perform both work and personal duties. About 75% of SBS had good job satisfaction; 59.6% of them used their time at home to connect with their friends and families and 53.2% felt that the time spent with them was satisfying. The rate of burnout among SBS was 34%. Compared with non-SBS, SBS had 2.79 times the odds of bringing work home (OR: 2.79, 95% CI: 1.32–6.38, p = 0.003). However, SBS were found to be more satisfied with their incomes than non-SBS (OR: 1.77, 95% CI: 0.89–3.52, p = 0.09). No significant differences were found between SBS and non-SBS in terms of burnout (OR: 1.11, 95% CI: 0.53–2.27, p = 0.73), workload (OR: 1.56, 95% CI: 0.76–2.40, p = 0.25), sleep deprivation (OR: 1.20, 95% CI: 0.61–2.36, p = 0.63), WLB (OR: 1.43, 95% CI: 0.74–2.79, p = 0.27), job satisfaction (OR: 1.05, 95% CI: 0.50–2.36, p = 1), and quality time with their families (OR: 0.78, 95% CI: 0.39–1.56, p = 0.51). Conclusion: SBS show signs of significant un-wellness, particularly in terms of workloads and balancing work and non-work activities. Personal life activities are suffering in at least half of SBS with burnout being prevalent. The profession and organizations like hospitals and universities should take measures to realign workloads by creating strategies that enhance surgeons' abilities to achieve personal goals around work and non-work-related activities. Organized skull base surgery societies can take a leadership role in advocating for better wellness strategies globally. Publication History Article published online: 01 February 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.127
GPT teacher head0.286
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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