Evaluating implicit gender bias at Canadian otolaryngology meetings through use of professional title
Bibliographic record
Abstract
Abstract Objectives Increasing numbers of women enter medical school annually. The number of female physicians in leadership positions has been much slower to equalize. There are also well‐documented differences in the treatment of women as compared to men in professional settings. Female presenters are less likely to be introduced by their professional title (“Doctor”) for grand rounds and conferences, especially with a man performing the introduction. This study reviewed the Canadian Society of Otolaryngology–Head and Neck Surgery (CSOHNS) meetings from 2017 to 2020 to determine the proportion of presenters introduced by their professional title and whether this varied by gender. Methods Recordings from CSOHNS meetings were reviewed and coded for introducer and presenter demographics, including leadership positions and gender. Chi‐squared tests of proportion and multivariate logistic regression was used to compare genders and identify factors associated with professional versus unprofessional forms of address. Results No significant association was found between professional title use and introducer or presenter gender. Female presenters were introduced with professional title 69.6% of the time, while male presenters were introduced with professional title 67.6% of the time (P = 0.69). Residents were introduced with a professional title with the most frequency (75.8%), while attending staff were introduced with a professional title with the least frequency (63.0%) (P = 0.02). Conclusions The lack of gender bias in speaker introductions at recent CSOHNS meetings demonstrates progress in achieving gender equity in medicine. Research efforts should continue to define additional forms of unconscious bias that may be contributing to gender inequity in leadership positions.
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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 | 0.025 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".