Insights from North American radiology grand rounds: Determining patterns of gender bias in professional introductions
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the impact of moderator and speaker gender, as well as geographic location, on the use of professional titles during introductions in radiology grand rounds. Specifically, the study aimed to investigate potential gender disparities in how moderators introduce speakers, focusing on the use of formal titles such as "Doctor" compared to informal name-based introductions. METHODS: The study utilized English-language radiology grand rounds video recordings from seven institutions in Canada and the United States of America (USA) that were chosen due to their publicly available videos. The gender of the moderator and speaker and the type of title introduction the speaker received from the moderator (introducing them as "Doctor" or their name followed by their degree credentials or their first name only). Chi-square and Fisher's Exact tests were used to analyze the correlation between demographic variables (moderator and speaker gender, and country) and the chosen style of introduction (title usage). RESULTS: The study analyzed 250 speaker introductions in radiology grand rounds presentations at institutions in Canada and the USA. The professional title "Doctor" was used to introduce speakers 160 out of 250 instances (64.0 %) and significant gender disparities were found in how male moderators introduced speakers. Male moderators used the professional title "Doctor" to introduce male speakers 71.9 % of the time but did so for female speakers only 29.6 % of the time (χ²(1, N = 168) = 27.0, p < 0.001). Additionally, male moderators were more likely to introduce female speakers by "Name only" (44.4 %) compared to male speakers (18.4 %), (χ²(1, N = 168) = 12.59, p < 0.001). CONCLUSION: Although the title "Doctor" was used to introduce speakers the majority of the time, it was observed that male moderators are more likely to introduce male speakers with the title "Doctor" than female speakers, highlighting a potential gender bias in the recognition of professional status. However, female moderators were shown to introduce both male and female speakers as "Doctor" the majority of the time. Promoting equitable recognition across genders requires addressing these dynamics in professional environments.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".