Prevalence of gender-based and sexual harassment within orthopedic surgery in Canada
Bibliographic record
Abstract
BACKGROUND: Gender-based and sexual harassment are prevalent in the medical profession. We aimed to quantify the prevalence of such behaviours within orthopedic surgery in Canada and to identify any risk factors for experiencing gender-based or sexual harassment in the workplace. METHODS: In collaboration with the Canadian Orthopaedic Association, we conducted a Canada-wide email questionnaire survey in June 2019 of all orthopedic surgeons registered with the Canadian Orthopaedic Association and the Canadian Orthopaedic Residents' Association. The development of our questionnaire was informed by a review of the literature and published surveys on gender-based and sexual harassment, and consultation with researchers in intimate partner violence. We conducted a multivariable logistic regression analysis to identify risk factors for harassment. RESULTS: Of the 1783 surgeons invited to participate, 465 returned the questionnaire (response rate 26.1%); the response rate was 48.1% for females and 22.1% for males. Overall, 331/433 respondents (76.4%, 95% confidence interval [CI] 72%-80%) and 315/423 respondents (74.5%, 95% CI 70%-78%) reported having experienced at least 1 occurrence of gender-based and sexual harassment, respectively. Women were significantly more likely than men to have experienced both gender-based and sexual harassment (odds ratio [OR] 16.2, 95% CI 4.8-54.0, and OR 2.2, 95% CI 1.2-4.0, respectively). Respondents who identified as nonwhite were significantly less likely than those who identified as white to have experienced gender-based harassment (OR 0.5, 95% CI 0.3-0.99). CONCLUSION: The prevalence of gender-based and sexual harassment is high within Canadian orthopedic surgery, and women are at highest risk for experiencing harassment. The results may provide the impetus for orthopedic societies to develop action plans and to re-examine and enforce policies to address these damaging behaviours appropriately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".