Additional Considerations for the Expert–Generalist Model
Bibliographic record
Abstract
To the Editor: We applaud Dr. Fins’s1 suggestion to create an expert–generalist “track.” In fact, we think so highly of this idea that we brought it to fruition 20 years ago in the University of Michigan Department of Family Medicine (UMDFM). Several other large academic departments of family medicine have also pursued this approach. UMDFM is relatively large by family medicine standards with roughly 90 full-time faculty members, an essential feature that allows for recruiting or training faculty members with a wide range of special skills within what remains a fundamentally generalist discipline. Some of these special skills are supported by certificates of added qualification from the American Board of Family Medicine (e.g., sports medicine, geriatrics). Others are developed without formal certification, but certification is subsequently achieved. Some skills remain more informal but require substantial focused training (e.g., hospice and palliative medicine, hospitalist care, adolescent medicine, integrative medicine, women’s health care including operative obstetrics). We offer the following observations based on our experiences. Faculty members with special expertise are critical teaching resources for students, residents, and fellows and make very different contributions to learners than do traditional subspecialists. “Expert–generalist” faculty members bridge for both teaching and clinical purposes what is often a large gap between primary care physicians or trainees and subspecialists, as Dr. Fins notes. Some primary care physicians struggle to reconcile their generalist value system with special knowledge and skills. We have long emphasized that what defines generalists is not what they do, but how they think, a critical foundation of this approach. A primary care sports physician is not an inadequately trained orthopedic surgeon, but a family physician who takes a comprehensive and holistic approach to a wide range of problems related to exercise and sports, including nonmusculoskeletal problems. We have found no resistance from residents to the additional training needed. Residents value special expertise and the credentials that come with it. They also understand that, somewhat paradoxically, such skills narrow their academic career options. We also oppose the shortening of undergraduate medical education, when espoused as appropriate only for those pursuing primary care careers. We believe, as do many academic family physicians, that primary care is as complex as subspecialty practice, albeit in different ways.2 Dr. Fins writes from the perspective of general internal medicine, which exists within a highly subspecialized medical discipline. We believe the experience of academic family medicine can contribute to the development of his ideas. Thomas L. Schwenk, MD Professor of family medicine and dean, University of Nevada School of Medicine, and vice president for health sciences, University of Nevada, Reno, Reno, Nevada; [email protected] Lee A. Green, MD, MPH Professor and chair, Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada. Philip Zazove, MD, MM Professor and George A. Dean, MD, Chair of Family Medicine, Department of Family Medicine, University of Michigan Medical Center, Ann Arbor, Michigan.
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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 | 0.030 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.037 | 0.059 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".