Reexamining Outcomes of the Primary Care Residency Expansion
Bibliographic record
Abstract
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]
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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 | 0.021 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".