Sex-Related Differences in Speaker Introductions at Ophthalmology Grand Rounds
Notice bibliographique
Résumé
PURPOSE: Sex bias remains a pervasive reality in academic medicine, often reflected in subtle linguistic choices, which can skew perceptions of competence and perpetuate workplace inequity. This study aims to investigate the relationship between host sex, speaker sex, and speaker introduction practices in ophthalmology grand rounds events. DESIGN: Cross-sectional study. METHODS: Publicly accessible videos of English-language ophthalmology grand rounds and other teaching events uploaded by academic institutions in the United States and Canada from January 2019 to June 2024 were analyzed by two independent reviewers. The primary outcome was the proportion of male and female speakers introduced with the formal title "Dr." by the event host. Secondary outcomes included the proportion of male and female speakers introduced with their academic degrees, current academic appointments, awards or achievements, and research interests. Univariable and multivariable logistic regressions adjusted for the speaker's degree type(s), academic appointment, and affiliation were performed using Stata v17.0. RESULTS: Of 1,450 videos screened, 399 speaker introductions across 298 ophthalmology teaching sessions were analyzed. The formal title "Dr." was employed by the event host in 75.2% (n = 300/399) of speaker introductions. In multivariable analysis, female speakers were significantly less likely to be introduced by their formal title (OR = 0.55, 95% CI: 0.25-0.78, P < .001), academic degrees (OR = 0.61, 95% CI: 0.35-0.97, P = .03) and their awards or achievements (OR = 0.62, 95% CI: 0.35-0.95, P = .04) compared to male speakers. Interaction terms between speaker and host sex were significant for formal title use (P = .03) and academic degrees (P = .04), prompting subgroup analyses by host sex. Findings were consistent when stratified by male hosts, while there was no difference in the likelihood of introducing male or female speakers with their formal titles, academic degrees, or awards/achievements when introduced by female hosts. Female speakers were significantly more likely to present on nonclinical topics compared to male speakers (OR = 2.39, 95% CI: 1.36-4.79, P < .001). CONCLUSIONS: When introduced by male hosts, female speakers were less likely to be addressed using a formal title compared with male speakers, while no significant differences were observed when female hosts introduced speakers of either sex. A standardized approach to introducing speakers may be beneficial in mitigating sex biases during grand rounds and other academic events.
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 | 0,003 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».