MétaCan
Menu
Back to cohort

Insights from North American radiology grand rounds: Determining patterns of gender bias in professional introductions

2024· article· en· W4404733508 on OpenAlex
Sonali Sharma, Ryan S. Huang, Aleena Malik, Hephzibah Bomide, Charlotte Portia Sum-Wai Lee, Faisal Khosa, Charlotte J. Yong‐Hing

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCurrent Problems in Diagnostic Radiology · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsVancouver General HospitalBC Cancer AgencyUniversity of TorontoUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsMedicineGender biasMEDLINERadiologyMedical education

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.354
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it