Distribution of Psychological Instability Among Surgeons
Bibliographic record
Abstract
BACKGROUND: High emotional instability (i.e., neuroticism) is associated with poor mental health. Conversely, traumatic experiences may increase neuroticism. Stressful experiences such as complications are common in the surgical profession, with neurosurgeons being particularly affected. We compared the personality trait neuroticism between physicians in a prospective cross-sectional study. METHODS: We used an online survey using the Ten-Item Personality Inventory, an internationally validated measure of the 5-factor model of personality dimensions. It was distributed to board-certified physicians, residents, and medical students in several European countries and Canada (n = 5148). Multivariate linear regression was used to model differences between surgeons, nonsurgeons, and specialties with occasional surgical interventions with respect to neuroticism, adjusting for sex, age, age squared, and their interactions, then testing equality of parameters of adjusted predictions separately and jointly using Wald tests. RESULTS: With an expected variability within disciplines, average levels of neuroticism are lower in surgeons than nonsurgeons, especially in the first part of their career. However, the course of neuroticism across age follows a quadratic pattern, that is, an increase after the initial decrease. The acceleration of neuroticism with age is specifically significant in surgeons. Levels of neuroticism are lowest towards mid-career, but exhibit a strong secondary increase towards the end of the surgeon's career. This pattern seems driven by neurosurgeons. CONCLUSIONS: Despite initially lower levels of neuroticism, surgeons suffer a stronger increase of neuroticism together with age. Because, beyond well-being, neuroticism influences professional performance and health care systems costs, explanatory studies are mandatory to enlighten causes of this burden.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".