Graduate Medical Education Funding and Curriculum in Physical Medicine and Rehabilitation
Bibliographic record
Abstract
This national survey highlights graduate medical education funding sources for physical medicine and rehabilitation (PM&R) residency programs as well as perceived funding stability, alignment of the current funding and educational model, the need of further education in postacute care settings, and the practice of contemporary PM&R graduates as perceived by PM&R department/division chairs. Approximately half of the reported PM&R residency positions seem to be funded by Centers of Medicare and Medicaid Services; more than 40% of PM&R chairs believe that their residency program is undersized and nearly a quarter feel at risk for losing positions. A total of 30% of respondents report PM&R resident experiences in home health, 15% in long-term acute care, and 52.5% in a skilled nursing facility/subacute rehabilitation facility. In programs that do not offer these experiences, most chairs feel that this training should be included. In addition, study results suggest that most PM&R graduates work in an outpatient setting. Based on the results that chairs strongly feel the need for resident education in postacute care settings and that most graduates go on to practice in outpatient settings, there is a potential discordance for our current Centers of Medicare and Medicaid Services graduate medical education funding model being linked to the acute care setting.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".