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Enregistrement W4396733501 · doi:10.1001/jamanetworkopen.2024.10706

Sexual Harassment, Abuse, and Discrimination in Obstetrics and Gynecology

2024· review· en· W4396733501 sur OpenAlexaffabout
Ankita Gupta, Jennifer Thompson, Nancy E. Ringel, Shunaha Kim-Fine, Lindsay A. Ferguson, Stephanie V. Blank, Cheryl B. Iglesia, Ethan M. Balk, Angeles Alvarez Secord, Jeffrey F. Hines, Jubilee Brown, Cara L. Grimes

Notice bibliographique

RevueJAMA Network Open · 2024
Typereview
Langueen
DomaineSocial Sciences
ThématiqueDiversity and Career in Medicine
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésHarassmentObstetrics and gynaecologyObstetricsGynecologyMedicineSexual abusePsychologyMedical emergencyPregnancyPoison controlSuicide preventionNursingBiology

Résumé

récupéré en direct d'OpenAlex

Importance: Unlike other surgical specialties, obstetrics and gynecology (OB-GYN) has been predominantly female for the last decade. The association of this with gender bias and sexual harassment is not known. Objective: To systematically review the prevalence of sexual harassment, bullying, abuse, and discrimination among OB-GYN clinicians and trainees and interventions aimed at reducing harassment in OB-GYN and other surgical specialties. Evidence Review: A systematic search of PubMed, Embase, and ClinicalTrials.gov was conducted to identify studies published from inception through June 13, 2023.: For the prevalence of harassment, OB-GYN clinicians and trainees on OB-GYN rotations in all subspecialties in the US or Canada were included. Personal experiences of harassment (sexual harassment, bullying, abuse, and discrimination) by other health care personnel, event reporting, burnout and exit from medicine, fear of retaliation, and related outcomes were included. Interventions across all surgical specialties in any country to decrease incidence of harassment were also evaluated. Abstracts and potentially relevant full-text articles were double screened.: Eligible studies were extracted into standard forms. Risk of bias and certainty of evidence of included research were assessed. A meta-analysis was not performed owing to heterogeneity of outcomes. Findings: A total of 10 eligible studies among 5852 participants addressed prevalence and 12 eligible studies among 2906 participants addressed interventions. The prevalence of sexual harassment (range, 250 of 907 physicians [27.6%] to 181 of 255 female gynecologic oncologists [70.9%]), workplace discrimination (range, 142 of 249 gynecologic oncologists [57.0%] to 354 of 527 gynecologic oncologists [67.2%] among women; 138 of 358 gynecologic oncologists among males [38.5%]), and bullying (131 of 248 female gynecologic oncologists [52.8%]) was frequent among OB-GYN respondents. OB-GYN trainees commonly experienced sexual harassment (253 of 366 respondents [69.1%]), which included gender harassment, unwanted sexual attention, and sexual coercion. The proportion of OB-GYN clinicians who reported their sexual harassment to anyone ranged from 21 of 250 AAGL (formerly, the American Association of Gynecologic Laparoscopists) members (8.4%) to 32 of 256 gynecologic oncologists (12.5%) compared with 32.6% of OB-GYN trainees. Mistreatment during their OB-GYN rotation was indicated by 168 of 668 medical students surveyed (25.1%). Perpetrators of harassment included physicians (30.1%), other trainees (13.1%), and operating room staff (7.7%). Various interventions were used and studied, which were associated with improved recognition of bias and reporting (eg, implementation of a video- and discussion-based mistreatment program during a surgery clerkship was associated with a decrease in medical student mistreatment reports from 14 reports in previous year to 9 reports in the first year and 4 in the second year after implementation). However, no significant decrease in the frequency of sexual harassment was found with any intervention. Conclusions and Relevance: This study found high rates of harassment behaviors within OB-GYN. Interventions to limit these behaviors were not adequately studied, were limited mostly to medical students, and typically did not specifically address sexual or other forms of harassment.

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)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,967
Score d'incertitude au seuil0,609

Scores Codex et Gemma par catégorie

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

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 Opus0,099
Tête enseignante GPT0,396
Écart entre enseignants0,296 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations13
Publié2024
Routes d'admission2
Résumé présentoui

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