Association Between Average Annual US Medical Specialty Compensation and Percentage of Trainees in the Specialty Who Are Female
Bibliographic record
Abstract
ABSTRACT Background Female physicians have lower incomes than male physicians. While overall sex-based income disparities are dramatic, compensation differs considerably across specialties. A better understanding of the relationship between anticipated specialty-specific annual incomes and the proportion of females entering that specialty might help residency program directors argue for equity in specialty choice and income for female physicians. Objective We sought to determine the relationship between the percentage of females in the workforce entering a specialty and the average compensation of that specialty in 2023. Methods From a recent JAMA article, we obtained the characteristics and numbers of trainees engaged in 160 specialties or subspecialties within 13 489 graduate medical education programs in 2023; we aggregated those data into 50 specialties for which 2023 average annual self-reported compensation were publicly available from Doximity. We conducted a stepwise linear regression in which the specialty-specific proportion of trainees who were female, US medical school graduates, Canadian, Doctors of Osteopathy, American Indian or Alaska Native, Asian, Black, Hispanic or Latino, Pacific Islander, or White were used to predict the specialty-specific average annual income. We conducted the analysis in 2024. Results Each one percent increase in the specialty-specific percentage of female trainees was associated with a $5,301 decrease in average specialty-specific annual compensation, and each one percent increase in the US medical school graduate percentage was associated with a $3,821 increase. These 2 characteristics accounted for 78% of the adjusted explainable variance in average specialty-specific annual compensation. Conclusions Specialties with higher proportions of female trainees had lower average annual compensation rates.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".