Professional Identity Formation and the Clinician–Scientist: A Paradigm for a Clinical Career Combining Two Distinct Disciplines
Bibliographic record
Abstract
The clinician-scientist role is critical to the future of health care, and in 2010, the Carnegie Report on Educating Physicians focused attention on the professional identity of practicing clinicians. Although limited in number, published studies on the topic suggest that professional identity is likely a critical factor that determines career sustainability. In contrast to clinicians with a singular focus on clinical practice, clinician-scientists combine two major disciplines, clinical medicine and scientific research, to bridge discovery and clinical care. Despite its importance to advancing medical practice, the clinician-scientist career faced a variety of threats, which have been identified recently by the 2014 National Institutes of Health Physician Scientist Workforce. Yet, professional identity development in this career pathway is poorly understood. This Perspective focuses on the challenges to the clinician-scientist's professional identity and its development. First, the authors identify the particular challenges that arise from the different cultures of clinical care and science and the implications for clinician-scientist professional identity formation. Next, the authors synthesize insights about professional identity development within a dual-discipline career and apply their analysis to a discussion about the implications for clinician-scientist identity formation. Although not purposely developed to address identity formation, the authors highlight those elements within clinician-scientist training and career development programs that may implicitly support identity development. Finally, the authors highlight a need to identify empirically the elements that compose and determine clinician-scientist professional identity and the processes that shape its formation and sustainability.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".