Additional Considerations for the Expert–Generalist Model
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,102 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,013 |
| Communication savante | 0,008 | 0,016 |
| Science ouverte | 0,008 | 0,004 |
| Intégrité de la recherche | 0,037 | 0,059 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».