Mirror mirror on the ward, who’s the most narcissistic of them all? Pathologic personality traits in health care
Bibliographic record
Abstract
BACKGROUND: Stereotypes in medicine have become exaggerated for the purpose of workplace amusement. Our objective was to assess the levels of "dark triad" personality traits expressed by individuals working in different health care specialties in comparison with the general population. METHODS: We conducted a prospective, cross-sectional study within multiple departments of a UK secondary care teaching hospital. A total of 248 health care professionals participated, and 159 members of the general population were recruited as a comparison group. We measured 3 personality traits--narcissism, Machiavellianism and psychopathy--through the validated self-reported personality questionnaires Narcissistic Personality Inventory (NPI), MACH-IV and the Levenson Self-Report Psychopathy Scale (LSRP), respectively. RESULTS: Health care professionals scored significantly lower on narcissism, Machiavellianism and psychopathy (mean scores 12.0, 53.0 and 44.7, respectively) than the general population (p < 0.001). Nursing professionals exhibited a significantly higher level of secondary psychopathy than medical professionals (p = 0.04, mean LSRP score 20.3). Within the cohort of medical professionals, surgeons expressed significantly higher levels of narcissism (p = 0.03, mean NPI score 15.0). INTERPRETATION: Health care professionals expressed low levels of dark triad personality traits. The suggestion that health care professionals are avaricious and untrustworthy may be refuted, even for surgeons.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".