The Nature of Excellent Clinicians at an Academic Health Science Center
Bibliographic record
Abstract
PURPOSE: To understand the nature of excellent clinicians at an academic health science center by exploring how and why excellent clinicians achieve high performance. METHOD: From 2008 to 2010, the authors conducted a qualitative study using a grounded theory approach. Members of the Clinical Advisory Committee in the Department of Pediatrics at the University of Toronto nominated peers whom they saw as excellent clinicians. The authors then conducted in-depth interviews with the most frequently nominated clinicians. They audio-recorded and transcribed the interviews and coded the transcripts to identify emergent themes. RESULTS: From interviews with 13 peer-nominated, excellent clinicians, a model emerged. Dominant themes fell into three categories: (1) core philosophy, (2) deliberate activities, and (3) everyday practice. Excellent clinicians are driven by a core philosophy defined by high intrinsic motivation and passion for patient care and humility. They refine their clinical skills through two deliberate activities-reflective clinical practice and scholarship. Their high performance in everyday practice is characterized by clinical skills and cognitive ability, people skills, engagement, and adaptability. CONCLUSIONS: A rich theory emerged explaining how excellent clinicians, driven by a core philosophy and engaged in deliberate activities, achieve high performance in everyday practice. This theory of the nature of excellent clinicians provides a holistic perspective of individual performance, informs medical education, supports faculty career development, and promotes clinical excellence in the culture of academic medicine.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".