What Are the Personality Types Among Emergency Medicine Physicians?
Bibliographic record
Abstract
Introduction Emergency medicine physicians work in high-stress environments that strain interpersonal skills, communication, and decision-making. Personality profile assessment tools have been used in educating the corporate world to enhance self-awareness, improve communication, and decrease conflict. Despite this, personality profile assessment tools have not been applied extensively within the emergency department context. As such, we explored whether Insights Discovery (Insights, Dundee, Scotland), a registered personality assessment tool, could contribute valuable understanding into the personality landscape of emergency medicine physicians and help tailor future educational interventions. Methods A cross-sectional survey was conducted via online administration of the Insights Discovery questionnaire to 30 attending emergency physicians of urban tertiary-care and community emergency departments of Calgary, Alberta, Canada. Results A disproportionately low number of fiery red personality types, typically described as competitive and strong-willed, existed among the study groups. No other significant differences were found between the proportions of other personality types or between physician characteristics such as gender or years of experience. Conclusion This study sheds early light on the personality characteristics of physicians within the emergency department environment, which may help individuals and departments tailor interventions to improve interpersonal communication and interactions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".