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Enregistrement W2791877237 · doi:10.2106/jbjs.17.01501

Where Are All the Women?

2018· letter· en· W2791877237 sur OpenAlexaboutno aff
Mary I. O’Connor

Notice bibliographique

RevueJournal of Bone and Joint Surgery · 2018
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueDiversity and Career in Medicine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

Commentary “There are only two options: Make progress or make excuses.”1 (Tony Robbins) Current Procedural Terminology (CPT) codes are sex, race, and ethnicity-blind. The finding by Holliday et al. that Medicare pays surgeons equally for total hip and knee replacement regardless of sex is not a surprise. Furthermore, commercial contracts for professional fee payments are not negotiated with different payment scales based on the personal characteristics of the orthopaedic surgeon. The full story of compensation, however, often is not told just by CPT codes. Different factors may contribute to the determination of a surgeon’s compensation, particularly at an academic medical center. Data from Jena et al. regarding physician salary in U.S. public medical schools that were adjusted for multiple factors (e.g., faculty rank, age, years since residency, specialty, publication count, total Medicare payments as a surrogate for clinical volume, etc.) confirmed a gender gap in compensation in orthopaedics, with men earning $368,070 and women earning $327,1172. Over the span of a career, this gap could total >$1,000,000, a substantial amount toward retirement security. Reasons for this gap are unclear and certainly multifactorial. Although as a society we believe that it is simply wrong for people to be paid differently for the same work, there may not be equal pay for equal work in orthopaedics, at least in academic medical centers. I am unaware of any data on gender analysis of compensation for orthopaedic surgeons in private practice. While the compensation question is an important one, the larger issue that the Holliday et al. paper raises is the substantial gender disparities in orthopaedics. We simply do not have enough women in the profession, and certain subspecialties are markedly underrepresented by women. Despite medical schools essentially reaching gender parity in 2001, orthopaedics had the third lowest percentage of women in residency programs (10.9%) in 2005, and the lowest percentage (14.8%) in 20153. Every other surgical specialty trains a higher percentage of women than orthopaedics. Moreover, our residency programs do not train women at equal rates: in the years 2009 to 2014, 30 programs had no female trainees4. Relative to subspecialty practice according to the American Academy of Orthopaedic Surgeons (AAOS) 2016 Orthopaedic Practice Survey, women self-report as orthopaedic specialists most commonly in pediatrics (22.1%), pediatric spine (15.6%), oncology (14.8%), and hand (13.5%); they self-report as specialists least commonly in adult spine (3.3%) and total joints (3.2%). Hence, the data in the Holliday et al. paper stating that only 1.9% of orthopaedic surgeons who submitted >10 Medicare claims for total knee arthroplasty (TKA) and 1.4% of orthopaedic surgeons who submitted >10 Medicare claims for total hip arthroplasty (THA) in 2013 were female are consistent with the AAOS data, and beg the question of whether we are encouraging (or discouraging) our women residents to pursue adult reconstructive fellowships. Our options are to make progress or make excuses. As Chair of the AAOS Diversity Advisory Board, I have repeatedly listened to the results of the AAOS surveys that asked fellows to rank priorities to guide AAOS leadership in allocation of limited resources. Diversity is never ranked as a priority by the majority of the fellows. We either don’t care (which I don’t believe), or don’t understand why diversity matters (which I do believe). Diversity of individuals and perspectives makes teams stronger and outcomes better. This is why we shouldn’t just train smart Caucasian males. There is ample evidence in the business world that companies with greater gender representation on executive boards are more financially successful; we intuit that these companies are making better decisions. Broader diversity of members mitigates the unconscious bias that each of us has as human beings. Unconscious bias is omnipresent in medicine as well—from the evaluation of physicians for compensation increases to surgical decision-making and patient compliance. While various studies in business have examined whether women ask or do not ask for increased compensation, the reality is that there is a compensation gap in business and, as previously discussed, with orthopaedic surgeons at academic medical centers. Structuring compensation on transparent objective metrics may improve compensation equity. Of greater concern to patients is the influence of unconscious bias on treatment recommendations. Women have more functional impairment and worse pain than men at the time of TKA, and postoperative function is not as favorable in women5. One reason may be that orthopaedic surgeons do not offer surgery to women at the same stage of disease, with women undergoing surgery when the disease is more severe. A fascinating study conducted in Ontario, Canada, utilized a standardized male and a standardized female patient with moderate knee osteoarthritis to gauge the recommendation for TKA. Borkhoff et al. showed that the odds of a family practice physician recommending TKA to the standardized male patient was 2 times that for the standardized female patient, and the odds of an orthopaedic surgeon recommending TKA to the male patient was 22 times that for the female patient6. There were insufficient numbers of female physicians in the Borkhoff et al. study to analyze if the gender of the provider impacted the results. How do we interpret these findings? Could it be that we, as orthopaedic surgeons (both women and men), believe that a man is more symptomatic than a woman, even if each describes the same level of pain and functional limitation as the standardized patients did in the Borkhoff et al. study? After all, we live in a society that has biased us to believe that women more readily voice their symptoms and have a lower pain threshold than men. And then there are the potential biases that we have relative to patients of color and to those with lower socioeconomic means. We must come to realize that our biases impact our patients. Clearly, additional research is needed to better understand the impact of gender, as well as race and ethnicity, on both physicians and patients regarding treatment recommendations and outcomes. Our nation is becoming more diverse. By 2042, the majority of the United States population will no longer be Caucasian. Disparities in health care are real. Orthopaedic surgery must become a profession that welcomes gender, racial, and ethnic diversity in order to attract the best and the brightest medical students and to best serve all of our patients.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,996
Score d'incertitude au seuil0,134

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,031
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,001
Études des sciences et des technologies0,0040,006
Communication savante0,0040,010
Science ouverte0,0060,003
Intégrité de la recherche0,0250,039
Charge utile insuffisante (le modèle a refusé de juger)0,0400,021

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,048
Tête enseignante GPT0,258
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineIncitatifs
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2018
Routes d'admission1
Résumé présentoui

Explorer davantage

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