Different but similar: personality traits of surgeons and internists—results of a cross-sectional observational study
Bibliographic record
Abstract
OBJECTIVES: Medical practice may attract and possibly enhance distinct personality profiles. We set out to describe the personality profiles of surgical and medical specialties focusing on board-certified physicians. DESIGN: Prospective, observational. SETTING: Online survey containing the Ten-Item Personality Inventory (TIPI), an internationally validated measure of the Five Factor Model of personality dimensions, distributed to board-certified physicians, residents and medical students in several European countries and Canada. Differences in personality profiles were analysed using multivariate analysis of variance and Canonical Linear Discriminant Analysis on age-standardised and sex-standardised z-scores of the personality traits. Single personality traits were analysed using robust t-tests. PARTICIPANTS: The TIPI was completed by 2345 board-certified physicians, 1453 residents and 1350 medical students, who also provided demographic information. RESULTS: Normal population and board-certified physicians' personality profiles differed (p<0.001). The latter scored higher on conscientiousness, extraversion and agreeableness, but lower on neuroticism (all p<0.001). There was no difference in openness to experience. Board-certified surgical and medical doctors' personality profiles were also different (p<0.001). Surgeons scored higher on extraversion (p=0.003) and openness to experience (p=0.002), but lower on neuroticism (p<0.001). There was no difference in agreeableness and conscientiousness. These differences in personality profiles were reproduced at other levels of training, that is, in students and training physicians engaging in surgical versus medical practice. CONCLUSION: These results indicate the existence of a distinct and consistent average 'physician personality'. Despite high variability within disciplines, there are moderate but solid and reproducible differences between surgical and medical specialties.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".