Sex-Related Differences in Speaker Introductions at Ophthalmology Grand Rounds
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".