Hot Off the Press: She Works Hard for the Money—Time's Up in Health Care
Notice bibliographique
Résumé
Salary disparity between men and women has existed for as long as it has been measured, and despite efforts such as the Equal Pay Act of 1963, this disparity continues to exist. This gap is seen across numerous professions, including law, marketing, administration, and medicine. In the United States, women working full time are typically paid just 80% of what men are paid.1-4 According to one 2010 analysis, the disparity in medicine is one of the highest for any professional industry, trailing only dentistry.5 Women now represent half of medical school graduates and 38% of faculty members in U.S. medical schools.6 After controlling for multiple factors, including specialty, age, faculty rank, and metrics of productivity, male physicians earned nearly $20,000 more per year than their female counterparts.7, 8 Within emergency medicine, studies have shown female faculty are paid 10% to 13% less than males.9, 10 This study asks if there is a difference in compensation for male and female academic emergency physicians practicing in the United States. It is a cross-sectional, observational study done over 4 years from 2013 to 2017 (excluding 2014). The reported compensation was the adjusted median annual base salary for physicians. Overall this was a well-done observational study performed in academic emergency departments across the United States. Within the sampled group they controlled for confounders such as work region, practice setting, and years at academic rank. However, this study has several limitations. First, the response rate of this survey is not certain because the survey is sent to the listserv of all AACEM (Association of Academic Chairs in Emergency Medicine) and AAAEM (Academy of Administrators in Academic Emergency Medicine) and we do not have denominator data for the survey. We are unsure whether the physicians sampled in each year of the survey were the same, as it was anonymized administrative data, rather than being provided by the individual physicians. Thus year to year the results may represent a different group of physicians. Also, the year 2014 was excluded because of a change in the group administering the survey (more details in podcast). Furthermore, the sample of physicians may not be representative of non–academic emergency physicians or those in other countries than the United States, thus raising questions about external validity. As the data is observational, it demonstrates an association, but not a clear cause and effect. There may be unobserved confounders. Despite these limitations, results of this study do fit with other available evidence regarding the gender pay gap. The median salary increase over the course of the study was greater for men ($226,746 in 2013 to $252,000 in 2017) than women ($217,000 in 2013 to $240,000 in 2017). Overall salaries increased across all 4 years studied with an overall increase of 10.8% (95% confidence interval [CI] = 9.6% to 12%). Women's salaries increased 10.6% (95% CI = 9.4% to 11.8%) while men's salaries increased 11.1% (95% CI = 10.2% to 12%). The overall difference in salary for males was higher and this was significant at all four time points (Z = 6.33, p < 0.001). This pay difference persisted in the predictive model controlling for covariates. An optimistic finding of this study was that between 2016 and 2017, women's salaries increased at a rate of 6.56% compared to 3.82% for men. Only time will tell if this is a true and sustainable change. At all time points, the proportion of respondents at higher academic ranks and higher salaries was always greater for men than women. With recruitment of new faculty, chairs, or their designees should institute a standard base pay rate based on rank and years of service. This does not include total compensation, which might include benefits, resources available to a faculty member, access to protected time, discretionary accounts, and relocation bonuses. With regard to elimination of these disparities in already established faculty, different targeted strategies would be required for parity of compensation. If you are motivated by, disturbed by, or interested in the findings of this study, pursue further study in this area so that we have more information to better define this problem. “What Do I Tell the Colleague?” correctly says complain to leadership and give [opportunity] to correct. (Do this via email, cc your private acct so you have a record … my clients have complained verbally and leadership denied it ever happened and then retaliated) Women have been complaining for years, and [are] often ignored and/or retaliated against. Pay gap studies [are] ignored. Internal HR, Title IX, AAEO, legal staff often engage in cover-ups or hire morally challenged outside “investigators” who routinely bury discrimination for their clients. My advice? Take it out of their hands. How? I'll tell you. File BOLI complaints (in Oregon), EEOC complaints (nationally), and lawsuits. Academic hospital employees can file @NIH complaints, @acgme complaints, Federal Office of Civil Rights complaints www2.ed.gov/about/offices/ … Internal complaints have been ineffective and punished. #TimesUp□ This is adjusted for clinical productivity? Don't see that on my initial read. Please correct me if I'm wrong, but I don't think the data could have adjusted for clinical productivity … the survey is filled out in such a way that linking individual faculty members’ salaries to clinical productivity would be impossible. From [para]graph 8: After controlling for multiple factors, including … metrics of productivity, male physicians earned nearly $20,000 more per year than their female counterparts … studies have shown female faculty are paid 10% to 13% less than males.9, 10 @AliRaja_MD Great find, @kemdvm! However, I'm not sure that “metrics of productivity” necessarily equates to “clinical productivity” instead of publications and other metrics of academic productivity. We all need to be more aware of this persistent pay gap and how it continues despite our best intentions. Although these data do not indicate the reasons for the gap, nor the means to fix it, we need to try to employ means of being more objective and consistent in the way that we allocate compensation. Every few years a review should be performed to look at salaries and other forms of compensation in physicians. We also need further research aimed at determining the underlying cause of the pay gap. This is to ensure that we correct any unexplained differences to make sure we have a safe and equitable environment.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,055 | 0,006 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».