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Enregistrement W4310948023 · doi:10.1001/jamasurg.2022.6431

Comparison of Male and Female Surgeons’ Experiences With Gender Across 5 Qualitative/Quantitative Domains

2022· letter· en· W4310948023 sur OpenAlexaff
Cheryl K. Zogg, Lyndsay A. Kandi, Hannah S. Thomas, Mary A. Siki, Ashley Y. Choi, Camila R. Guetter, C. B. Smith, Erica Maduakolam, Shreya Kondle, Sharon L. Stein, Elizabeth Shaughnessy, Nita Ahuja

Notice bibliographique

RevueJAMA Surgery · 2022
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueDiversity and Career in Medicine
Établissements canadiensUniversity of Toronto
Organismes subventionnairesNational Center for Advancing Translational SciencesNational Institute of General Medical SciencesNational Institute on Aging
Mots-clésMedicineQualitative propertyFamily medicineQualitative researchDemographyGerontology

Résumé

récupéré en direct d'OpenAlex

Importance: A growing body of literature has been developed with the goal of attempting to understand the experiences of female surgeons. While it has helped to address inequities and promote important programmatic improvements, work remains to be done. Objective: To explore how practicing male and female surgeons' experiences with gender compare across 5 qualitative/quantitative domains: career aspirations, gender-based discrimination, mentor-mentee relationships, perceived barriers, and recommendations for change. Design, Setting, and Participants: This national concurrent mixed-methods survey of Fellows of the American College of Surgeons (FACS) compared differences between male and female FACS. Differences between female FACS and female members of the Association of Women Surgeons (AWS) were also explored. A randomly selected 3:1 sample of US-based male and female FACS was surveyed between January and June 2020. Female AWS members were surveyed in May 2020. Exposure: Self-reported gender. Main Outcomes and Measures: Self-reported experiences with career aspirations (quantitative), gender-based discrimination (quantitative), mentor-mentee relationships (quantitative), perceived barriers (qualitative), and recommendations for change (qualitative). Results: A total of 2860 male FACS (response rate: 38.1% [2860 of 7500]) and 1070 female FACS (response rate: 42.8% [1070 of 2500]) were included, in addition to 536 female AWS members. Demographic characteristics were similar between randomly selected male and female FACS, with the notable exception that female FACS were less likely to be married (720 [67.3%] vs 2561 [89.5%]; nonresponse-weighted P < .001) and have children (660 [61.7%] vs 2600 [90.9%]; P < .001). Compared with female FACS, female AWS members were more likely to be younger and hold additional graduate degrees (320 [59.7%] were married; 238 [44.4%] had children). FACS of both genders acknowledged positive and negative aspects of dealing with gender in a professional setting, including shared experiences of gender-based harassment, discrimination, and blame. Female FACS were less likely to have had gender-concordant mentors. They were more likely to emphasize the importance of gender when determining career aspirations and prioritizing future mentor-mentee relationships. Moving forward, female FACS emphasized the importance of avoiding competition among female surgeons. They encouraged male surgeons to acknowledge gender bias and admit their potential role. Male FACS encouraged male and female surgeons to treat everyone the same. Conclusions and Relevance: Experiences with gender are not limited to supportive female surgeons. The results of this study emphasize the importance of recognizing the voices of all stakeholders involved when striving to promote workforce diversity and the related need to develop quality improvement/surgical education initiatives that enhance inclusion through open, honest discourse.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,151
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,003
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,161
Tête enseignante GPT0,413
Écart entre enseignants0,253 · 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 tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

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

En bref

Citations15
Publié2022
Routes d'admission1
Résumé présentoui

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