Exploring female otolaryngologists’ experiences with gender bias and microaggressions: A cross sectional Canadian survey
Bibliographic record
Abstract
BACKGROUND: Gender bias is behavior that shows favoritism towards one gender over another. Microaggressions are defined as subtle, often unconscious, discriminatory, or insulting actions that communicate demeaning or negative attitudes. Our objective was to explore how female otolaryngologists experience gender bias and microaggressions in the workplace. METHODS: Anonymous web-based cross-sectional Canadian survey was distributed to all female otolaryngologists (attendings and trainees) using the Dillman's Tailored Design Method from July to August of 2021. Quantitative survey included demographic data, validated 44-item Sexist Microaggressions Experiences and Stress Scale (MESS) and validated 10-item General Self-efficacy scale (GSES). Statistical analysis included descriptive and bivariate analysis. RESULTS: Sixty out of 200 participants (30% response rate) completed the survey (mean age 37 ± 8.3 years, 55.0% white, 41.7% trainee, 50% fellowship-trained, 50% with children, mean 9.2 ± 7.4 years of practice). Participants scored mild to moderate on the Sexist MESS-Frequency (mean ± standard deviation) 55.8 ± 24.2 (42.3% ± 18.3%), Severity 46.0 ± 23.9 (34.8% ± 18.1%), Total 104.5 ± 43.7 (39.6% ± 16.6%) and high on GSES (32.7 ± 5.7). Sexist MESS score was not associated with age, ethnicity, fellowship-training, having children, years of practice, or GSES. In the sexual objectification domain, trainees had higher frequency (p = 0.04), severity (p = 0.02) and total MESS (p = 0.02) scores than attendings. CONCLUSIONS: This was the first multicenter, Canada-wide study exploring how female otolaryngologists experience gender bias and microaggressions in the workplace. Female otolaryngologists experience mild to moderate gender bias, but have high self-efficacy to manage this issue. Trainees had more severe and frequent microaggressions than attendings in the sexual objectification domain. Future efforts should help develop strategies for all otolaryngologists to manage these experiences, and thereby improve the culture of inclusiveness and diversity in our specialty.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".