Gender-Related Discriminations in European Otolaryngology-Head and Neck Surgery: European Report
Bibliographic record
Abstract
Background Microaggressions are subtle verbal or behavioral insults (intentional or unintentional) that typically convey negative or hostile attitudes towards marginalized groups. We aim to study microaggressions and workplace culture amongst European otolaryngologist –head and neck surgeons (E-OTOHNS). The perception of “differential treatment” based on individual traits was used as a proxy for microaggressions. Methods European members of Young-Otolaryngologists of International Federation of Otorhinolaryngological Societies (IFOS) and Confederation of European Otorhinolaryngological Societies were surveyed regarding observed and personal experiences of microaggressions in the workplace as related to individual factors that comprise one’s identity; These factors included biological sex; disability; gender identity; language proficiency; citizenship; ethnicity; political belief; sexual orientation and socioeconomic status. Results A total of 230 E-OTOHNS completed the survey (17%), including 113 Women (49%) and 117 men (51%), respectively. The most common daily-to-monthly observed microaggressions were related to age (n = 177, 50.1%), biological sex (n = 105, 45.7%), and language proficiency (n = 67, 29.1%), respectively. Personal experiences of microaggression were related to professional rank (n = 80; 35.3%), age (n = 75; 32.6%), and biological sex (n = 63; 27.5%). Women self-reported significant higher proportions of personal experiences of microaggression related to ageage (40.7% vs 24.8%; P = 0.003), biological sex (41.6% vs 13.8%; P = 0.001), and professional rank (42.0% vs 28.7%; P = 0.049) compared to men. Similarly, Women self-reported higher rates of personal feeling of exclusion from their colleagues at the institution ( P = 0.036) than men and were more likely mistaken for another role in the hospital ( P = 0.004). Conclusions Woman European otolaryngologists, particularly those early in their careers, self-report higher proportions of observed or experienced microaggressions related to age, biological sex, and professional rank compared with male otolaryngologists. More efforts are needed in European academic Otolaryngology to reduce microaggressions, discriminations, and exclusions as more woman surgeons enter the medical workforce.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".