Reexamining Outcomes of the Primary Care Residency Expansion
Notice bibliographique
Résumé
To the Editor: Chen and colleagues1 present a compelling analysis of the Primary Care Residency Expansion (PCRE) program, but their findings do not align with their conclusion: “(A)n approach to primary care residency training expansion that relies on time-limited grants is unlikely to produce sustainable growth of the primary care pipeline.” In fact, the majority (54.9%) of respondents were likely or very likely to “sustain all their expanded positions” once grants expired. Of the 52 respondents who indicated any likelihood of continuing expanded positions, 37 (71.2%) reported they already had secured full or partial funding for 2016 and 2017—three to four years ahead of need. More than a quarter (26.9%) already had secured full funding to continue expanded positions beyond 2017—five years in advance—and all reported hospital funds as a source. It is not clear why the authors dismiss these responses as “unlikely” or “unrealistic” and advocate “systematic reform of GME financing” instead. They also incorrectly contend, “Both the [Council on Graduate Medical Education] COGME and the Medicare Payment Advisory Commission (MedPAC) have proposed reallocating existing Medicare GME funds paid to teaching hospitals in order to support more primary care residency positions and fewer specialty residency positions.” While COGME2 and MedPAC3 cite the importance of strengthening primary care training, neither recommends reallocating GME funding from specialty to primary care. The COGME report cited by the authors recommends, “Congress should continue funding for current GME positions, while increasing funding for additional positions.” Further, “increases in GME funding should be directed toward … high priority specialties,” specifying both primary care and specialty disciplines. Likewise, MedPAC suggests a “rigorous, independent workforce analysis is imperative to inform the most efficient use of these funds,” without predetermining the outcome. We share the authors’ concern that a grant-based GME financing system would destabilize physician training, and that as financial pressures grow, hospitals’ ability to absorb training costs could shrink. We need a multifaceted strategy that includes expanded Medicare GME support and recognizes the unique value of targeted initiatives supported by the Health Resources and Services Administration (HRSA), like PCRE. The Association of American Medical Colleges supports HRSA’s workforce programs, including PCRE. The authors’ findings underscore that faced with physician shortages, we should increase investments in successful physician workforce development programs, including, but not limited to, HRSA-funded initiatives. Tannaz Rasouli, MPH Director, Government Relations, Association of American Medical Colleges, Washington, DC. Peters D. Willson, MPPM Senior Specialist, Policy and Constituency Issues, Association of American Medical Colleges, Washington, DC; [email protected]
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 | 0,021 | 0,181 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,008 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».