Do Residents Who Train in Safety Net Settings Return for Practice?
Bibliographic record
Abstract
PURPOSE: To examine the relationship between training during residency in a federally qualified health center (FQHC), rural health clinic (RHC), or critical access hospital (CAH) and subsequent practice in these settings. METHOD: The authors identified residents who trained in safety net settings from 2001 to 2005 and in 2009 using 100% Medicare Part B claims files for FQHCs, RHCs, and CAHs and 2011 American Medical Association Masterfile residency start and end date histories. They used 2009 Medicare claims data to determine the relationship between this training and subsequent practice in safety net settings. RESULTS: The authors identified 662 residents who had a Medicare claim filed in their name by an RHC, 975 by an FQHC, and 1,793 by a CAH from 2001 to 2005 and in 2009. By 2009, that number of residents per year had declined for RHCs and FQHCs but increased substantially for CAHs. The percentage of physicians practicing in a safety net setting in 2009 who had trained in a similar setting from 2001 to 2005 was 38.1% (205/538) for RHCs, 31.2% (219/703) for FQHCs, and 52.6% (72/137) for CAHs. CONCLUSIONS: Using Medicare claims data, the authors identified residents who trained in safety net settings and demonstrated that many went on to practice in these settings. They recommend that graduate medical education policy support or expand training in these settings to meet the surge in health care demand that will occur with the enactment of the Affordable Care Act insurance provision in 2014.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".