Abstract 17129: Differences in Work Activities and Compensation of Male and Female Cardiologists in Community Practice in 2013
Bibliographic record
Abstract
Background: Despite laudable efforts to increase gender diversity in cardiology, much remains unknown about the experiences - including working activities and pay - of those women who have joined this still predominantly male specialty. Hypothesis: We hypothesized that a gender difference in compensation would exist on unadjusted analyses and that this could be explained by differences in the many personal, job, and practice characteristics measured in our dataset. Methods: Using the 2013 annual practice survey of MedAxiom, a subscription-based service provider for cardiology practices, we described personal, job, and practice characteristics of cardiologists from 161 practices and their salary by gender. Multivariable linear regression analysis and the Peters-Belson technique of labor economics were applied to evaluate gender differences. Results: Of 2679 subjects, 229 (8.5%) were female and 2450 male. Women were more likely to have specialized in general/non-invasive cardiology (53.1% vs 28.2%), and a lower proportion (11.4% vs 39.3%) reported an interventional subspecialty compared to men. Numerous job characteristics differed by gender, including the proportion working full-time (79.9% of women vs 90.9% of men, p<0.001), number of half-days worked (median 422 for women vs 433 for men, p=0.001), and wRVUs generated (median 7430 for women vs 9301 for men, p<0.001). Median salary was $394,586 (IQR: $256,064; $518,277) among women and $502,251 ($381,417; $621,306) among men. Peters-Belson analysis revealed that the women in this sample would have been expected to have a mean salary of $432,631, based on their productivity and other characteristics, had they been male, but the actual observed mean salary among women was $400,882 (unexplained difference=$31,749, 95% CI $16,303 - $48,028). Multivariable linear regression analysis yielded significant results similar in direction and magnitude. Conclusions: This study provides novel information about the diverse jobs held by men and women practicing cardiology in the US today. The observed gender difference in compensation, which remained substantial and significant even after adjusting for differences in multiple measures of productivity and job activity, merits attention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".