Reply to commentary to: Gender and rank salary trends among academic dermatologists
Notice bibliographique
Résumé
We thank Lipner et al. for their interest in our article and appreciate their effort in highlighting the contribution of gender in salary disparities among Veterans Affairs (VA) dermatologists.In their recently published article, Do and Lipner (2020) described the discrepancies in compensation for male and female VA dermatologists.Multivariate analysis showed that, overall, gender was not a significant contributor to VA dermatologists' salaries, and instead h-index, academic rank, and years since graduation were significant contributors.Their study found that true gender-based salary disparity was only noted regionally, specifically in the Midwest.We concur that female dermatologists are underrepresented in higher academic ranks, and a significant salary gap remains prevalent despite a narrowing of the gap between 2013 and 2018.These trends suggest a need for further studies over a longer time period that incorporate the previously mentioned factors affecting salary to identify the extent of gender-based salary gaps.Our paper reports the results of a pilot study using the Faculty Salary Survey database.We discussed how the lack of consideration for faculty salary based on full-time equivalent, geographic influences, clinical versus nonclinical faculty, and academic tracks are limitations of this database.The challenge in performing the same analysis using the Association of American Medical Colleges faculty database is, unlike the VA data, the lack of access to complete individual demographic data.This precludes a comprehensive analysis of the confounding factors Lipner et al. mention, such as h-index and other academic merits.We also agree that measures are likely being implemented to address gender salary gaps, as the commenters mention a higher median salary growth rate in women versus men in some ranks.However, despite the trend towards closing this gap observed in the 5-year period, there remains an unequal distribution of higher academic ranks between men and women.Many academic faculty members have employment at multiple nearby institutions.For example, a faculty member can have appointments and salary sources at the university medical center, VA, or state children's hospital.Various factors may contribute to the existence and size of gender discrepancies within different institutions.A larger-scale study than ours and the commenters' that compares salary sources and additional confounding factors is needed.We also need increased transparency and more accuracy in reporting and accessing other incentives and outside sources of income to have a more complete picture of compensation discrepancies between male and female dermatologists.
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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 | 0,005 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,049 | 0,036 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,007 |
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 ».