Does doctors’ personality differ from those of patients, the highly educated and other caring professions? An observational study using two nationally representative Australian surveys
Bibliographic record
Abstract
OBJECTIVES: Personality differences between doctors and patients can affect treatment outcomes. We examine these trait disparities, as well as differences across medical specialities. DESIGN: Retrospective, observational statistical analysis of secondary data. SETTING: Data from two data sets that are nationally representative of doctors and the general population in Australia. PARTICIPANTS: We include 23 358 individuals from a representative survey of the general Australian population (with subgroups of 18 705 patients, 1261 highly educated individuals and 5814 working in caring professions) as well as 19 351 doctors from a representative survey of doctors in Australia (with subgroups of 5844 general practitioners, 1776 person-oriented specialists and 3245 technique-oriented specialists). MAIN OUTCOME MEASURES: Big Five personality traits and locus of control. Measures are standardised by gender, age and being born overseas and weighted to be representative of their population. RESULTS: Doctors are significantly more agreeable (a: standardised score -0.12, 95% CIs -0.18 to -0.06), conscientious (c: -0.27 to -0.33 to -0.20), extroverted (e: 0.11, 0.04 to 0.17) and neurotic (n: 0.14, CI 0.08 to 0.20) than the general population (a: -0.38 to -0.42 to -0.34, c: -0.96 to -1.00 to -0.91, e: -0.22 to -0.26 to -0.19, n: -1.01 to -1.03 to -0.98) or patients (a: -0.77 to -0.85 to -0.69, c: -1.27 to -1.36 to -1.19, e: -0.24 to -0.31 to -0.18, n: -0.71 to -0.76 to -0.66). Patients (-0.03 to -0.10 to 0.05) are more open than doctors (-0.30 to -0.36 to -0.23). Doctors have a significantly more external locus of control (0.06, 0.00 to 0.13) than the general population (-0.10 to -0.13 to -0.06) but do not differ from patients (-0.04 to -0.11 to 0.03). There are minor differences in personality traits among doctors with different specialities. CONCLUSIONS: Several personality traits differ between doctors, the population and patients. Awareness about differences can improve doctor-patient communication and allow patients to understand and comply with treatment recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".