Canadian Women in Otolaryngology—Head and Neck Surgery part 1: the relationship of gender identity to career trajectory and experiences of harassment
Bibliographic record
Abstract
Abstract Introduction Women in surgical specialties face different challenges than their male peers. However, there is a paucity of literature exploring these challenges and their effects on a Canadian surgeon’s career. Methods A REDCap® survey was distributed to Canadian Otolaryngology–Head and Neck Surgery (OHNS) staff and residents in March 2021 using the national society listserv and social media. Questions examined practice patterns, leadership positions, advancement, and experiences of harassment. Gender differences in survey responses were explored. Results 183 completed surveys were obtained, representing 21.8% of the Canadian society membership [838 members with 205 (24.4%) women]. 83 respondents self-identified as female (40% response rate) and 100 as male (16% response rate). Female respondents reported significantly fewer residency peers and colleagues identifying as their gender (p < .001). Female respondents were significantly less likely to agree with the statement “My department had the same expectations of residents regardless of gender” (p < .001). Similar results were observed in questions about fair evaluation, equal treatment, and leadership opportunities (all p < .001). Male respondents held the majority of department chair (p = .028), site chief (p = .011), and division chief positions (p = .005). Women reported experiencing significantly more verbal sexual harassment during residency (p < .001), and more verbal non-sexual harassment as staff (p = .03) than their male colleagues. In both female residents and staff, this was more likely to originate from patients or family members (p < .03). Discussion There is a gender difference in the experience and treatment of OHNS residents and staff. By shedding light on this topic, as a specialty we can and must move towards greater diversity and equality. Graphical Abstract
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".