Physician Wages Across Specialties
Bibliographic record
Abstract
BACKGROUND: Disparities in remuneration between primary care and other physician specialties may impede health care reform by undermining the sustainability of a primary care workforce. Previous studies have compared annual incomes across specialties unadjusted for work hours. Wage (earnings-per-hour) comparisons could better inform the physician payment debate. METHODS: In a cross-sectional analysis of data from 6381 physicians providing patient care in the 2004-2005 Community Tracking Study (adjusted response rate, 53%), we compared wages across broad and narrow categories of physician specialties. Tobit and linear regressions were run. Four broad specialty categories (primary care, surgery, internal medicine and pediatric subspecialties, and other) and 41 specific specialties were analyzed together with demographic, geographic, and market variables. RESULTS: In adjusted analyses on broad categories, wages for surgery, internal medicine and pediatric subspecialties, and other specialties were 48%, 36%, and 45% higher, respectively, than for primary care specialties. In adjusted analyses for 41 specific specialties, wages were significantly lower for the following than for the reference group of general surgery (wage near median, $85.98): internal medicine and pediatrics combined (-$24.36), internal medicine (-$24.27), family medicine (-$23.70), and other pediatric subspecialties (-$23.44). Wage rankings were largely impervious to adjustment for control variables, including age, race, sex, and region. CONCLUSIONS: Wages varied substantially across physician specialties and were lowest for primary care specialties. The primary care wage gap was likely conservative owing to exclusion of radiologists, anesthesiologists, and pathologists. In light of low and declining medical student interest in primary care, these findings suggest the need for payment reform aimed at increasing incomes or reducing work hours for primary care physicians.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".