Compensation Disparities by Gender in Internal Medicine
Bibliographic record
Abstract
Letters6 November 2018Compensation Disparities by Gender in Internal MedicineSusan Read, PhD, Renee Butkus, BA, Arlene Weissman, PhD, and Darilyn V. Moyer, MDSusan Read, PhDAmerican College of Physicians, Philadelphia, Pennsylvania (S.R., A.W., D.V.M.), Renee Butkus, BAAmerican College of Physicians, Washington, DC (R.B.), Arlene Weissman, PhDAmerican College of Physicians, Philadelphia, Pennsylvania (S.R., A.W., D.V.M.), and Darilyn V. Moyer, MDAmerican College of Physicians, Philadelphia, Pennsylvania (S.R., A.W., D.V.M.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-0693 SectionsSupplemental MaterialAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Background: Despite progress toward gender diversity in the U.S. physician workforce, disparities in compensation and career advancement persist. Studies document that women earn substantially less than men after adjustment for specialty, hours worked, experience, and practice characteristics (1–3). Recent data on physician compensation by gender in internal medicine could help explain and address disparities within this specialty.Objective: To describe physician compensation by gender among U.S. American College of Physicians (ACP) internists.Methods: We performed a cross-sectional survey of a nationally representative panel of ACP nonstudent members in the United States. Women make up 37% of ACP membership. The survey ...References1. Jena AB, Olenski AR, Blumenthal DM. Sex differences in physician salary in US public medical schools. JAMA Intern Med. 2016;176:1294-304. [PMID: 27400435] doi:10.1001/jamainternmed.2016.3284 CrossrefMedlineGoogle Scholar2. Ly DP, Seabury SA, Jena AB. Differences in incomes of physicians in the United States by race and sex: observational study. BMJ. 2016;353:i2923. [PMID: 27268490] doi:10.1136/bmj.i2923 CrossrefMedlineGoogle Scholar3. Jagsi R, Griffith KA, Stewart A, Sambuco D, DeCastro R, Ubel PA. Gender differences in the salaries of physician researchers. JAMA. 2012;307:2410-7. [PMID: 22692173] doi:10.1001/jama.2012.6183 CrossrefMedlineGoogle Scholar4. Association of American Medical Colleges. 2016 Physician Specialty Data Report: Executive Summary. Washington, DC: Association of American Medical Colleges; 2016. Accessed at www.aamc.org/download/471786/data/2016physicianspecialtydatareportexecutivesummary.pdf on 22 December 2017. Google Scholar5. Association of American Medical Colleges. Matriculating Student Questionnaire: 2017 All Schools Summary Report. Washington, DC: Association of American Medical Colleges; 18 December 2017. Accessed at www.aamc.org/download/485324/data/msq2017report.pdf on 29 January 2018. Google Scholar Author, Article, and Disclosure InformationAffiliations: American College of Physicians, Philadelphia, Pennsylvania (S.R., A.W., D.V.M.)American College of Physicians, Washington, DC (R.B.)See also: Editorial comment (page 654).Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-0693.Reproducible Research Statement:Study protocol, statistical code, and data set: Available from Dr. Weissman (e-mail, [email protected]org).This article was published at Annals.org on 7 August 2018. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoUnequal Pay for Equal Work: Where Are We Now? Milda R. Saunders and Barbara J. 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Saunders, MD, MPH and Barbara J. Turner, MD, MSEd 6 November 2018Volume 169, Issue 9Page: 658-661KeywordsGender discriminationHospitalistsPatientsRacial and ethnic issuesResearch designSexual identityStatistical dataSub-specialty careSurvey researchTaxes ePublished: 7 August 2018 Issue Published: 6 November 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".