Perpetrators of Gender-Based and Sexual Harassment in the Field of Orthopaedic Surgery
Bibliographic record
Abstract
The prevalence of gender-based and sexual harassment in the field of orthopaedic surgery in Canada is high. Previous research in other jurisdictions has identified the most common perpetrators of harassment to be senior surgeons or directors. We aimed to identify the most frequent perpetrators of gender-based and sexual harassment in orthopaedic surgery in Canada. METHODS: We conducted a Canada-wide survey of all orthopaedic surgeons registered with the Canadian Orthopaedic Association and the Canadian Orthopaedic Residents' Association. The development of our 116-item questionnaire was informed by a review of the literature and other published gender-based and sexual harassment surveys. Descriptive analyses, including frequency counts with associated 95% confidence intervals (CIs), are reported for all data. RESULTS: Of the 465 survey respondents, the median age was 43 years (interquartile range, 35 to 59) and respondents were most commonly male (72%), White (81%), married (77%), and staff orthopaedic surgeons (68%). Peers were identified as the most common perpetrators of gender-based harassment (55%, 95% CI, 50 to 59), and patients were identified as the most common perpetrators of sexual harassment (48%, 95% CI, 43 to 52). Women were more likely to report direct supervisors or patients as the perpetrators of gender-based and sexual harassment, and men reported peers as the most common perpetrators. CONCLUSION: Orthopaedic surgery peers and patients are the most commonly reported perpetrators of gender-based and sexual harassment in Canada. The results of this study may be helpful to institutions in designing and focusing educational programs and/or policies and procedures to help reduce harassment incidents in the training and work 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".