Bibliographic record
Abstract
PURPOSE: The overall distribution of physicians in the United States is uneven, with concentrations in urban areas while some rural places have proportionately very few. This report examines the movement of physicians who have completed their training and choose to move from one location to another. METHOD: The analysis linked the locations of practice of physicians practicing in the 50 U.S. states in 2006 and 2011 using data from the American Medical Association Physician Masterfile. Age, gender, location practice, activity status, and specialty were included in the data. Physicians who changed address in the five-year period were identified and were compared with nonmovers using descriptive statistics. A summary logistic regression of movers compared with nonmovers was performed to assess the most important correlates of migration. RESULTS: The overall rate of county-to-county relocation for physicians was 19.8% for the five-year period 2006-2011. Analyses indicated that older, male, and urban physicians were less likely to move; that physicians with osteopathic training were more likely to move; and that surgeons and primary care physicians were less likely to move compared with other specialists. CONCLUSIONS: The physician workforce in the United States migrates from place to place, and this movement determines the local supply of practitioners at any given time. Programs that intend to influence the local supply of doctors should account for this background tendency to relocate practice in order to achieve goals of more equal geographic distribution.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".