Discrimination, harassment, and intimidation amongst otolaryngology—head and neck surgeons in Canada
Bibliographic record
Abstract
BACKGROUND: Understanding mistreatment within medicine is an important first step in creating and maintaining a safe and inclusive work environment. The objective of this study was to quantify the prevalence of perceived workplace mistreatment amongst otolaryngology-head and neck surgery (OHNS) faculty and trainees in Canada. METHODS: This national cross-sectional survey was administered to practicing otolaryngologists and residents training in an otolaryngology program in Canada during the 2020-2021 academic year. The prevalence and sources of mistreatment (intimidation, harassment, and discrimination) were ascertained. The availability, awareness, and rate of utilization of institutional resources to address mistreatment were also studied. RESULTS: The survey was administered to 519 individuals and had an overall response rate of 39.1% (189/519). The respondents included faculty (n = 107; 56.6%) and trainees (n = 82; 43.4%). Mistreatment (intimidation, harassment, or discrimination) was reported in 47.6% of respondents. Of note, harassment was reported at a higher rate in female respondents (57.0%) and White/Caucasian faculty and trainees experienced less discrimination than their non-White colleagues (22.7% vs. 54.5%). The two most common sources of mistreatment were OHNS faculty and patients. Only 14.9% of those experiencing mistreatment sought assistance from institutional resources to address mistreatment. The low utilization rate was primarily attributed to concerns about retribution. INTERPRETATION: Mistreatment is prevalent amongst Canadian OHNS trainees and faculty. A concerning majority of respondents reporting mistreatment did not access resources due to fear of confidentiality and retribution. Understanding the source and prevalence of mistreatment is the first step to enabling goal-directed initiatives to address this issue and maintain a safe and inclusive working environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".