The Most Important Attribute: Stakeholder Perspectives on What Matters Most in a Physician
Bibliographic record
Abstract
Background: Most people can think of important attributes that they believe physicians should have. The canmeds framework defines domains of attributes in medical training (Leader, Medical Expert, Scholar, Communicator, Advocate, Collaborator, and Professional). Whether some are more valued by various stakeholders is unknown. Previous research has shown that patients can receive suboptimal care if physician and patient expectations of a health care encounter differ. In the present study, we sought to identify what various stakeholders identified as the single most important attribute for a physician to possess. Methods: A simple survey asked the question "What is the single most important attribute a physician should have?" at a single academic teaching hospital and affiliated medical school. The survey was administered to medical students, doctors, nurses, patients, and caregivers. Age and sex were also collected. Responses were assigned to domains and analyzed to identify trends. The primary outcome is a descriptive analysis of the findings. Results: = 9. Compared with men, women chose attributes in the Caring domain more frequently (64% vs. 49%), although that domain was the most popular for both sexes. Medical students were less likely to highly value Communicator attributes. Conclusions: All stakeholder group identified attributes in the Caring domain as being most important. Although all canmeds roles are important, our research highlights the priorities of stakeholders.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".