#MeTooOrthopaedics: A protocol to determine the prevalence of gender-based and sexual harassment in the field of orthopaedic surgery
Bibliographic record
Abstract
Background. Research shows that gender-based and sexual harassment are prevalent within training and practicing medical establishments, with differences across specific specialties. Orthopaedic surgery remains a male-dominated field, with recent statistics reporting women representing only 5% and 12% of orthopaedic surgeons in the United States and Canada, respectively. This represents a significant gender discrepancy and, although there may be many reasons why this discrepancy exists, it is justified to explore whether gender-based and sexual harassment may contribute to this disparity. Objectives. This study aims to determine the prevalence of gender-based and sexual harassment in orthopaedic surgery, and to determine the impact of these experiences on female orthopaedic surgeons. It will also explore the association of such gender-based and sexual harassment experiences with certain demographic factors. Methods. We will conduct a cross-sectional survey of female orthopaedic surgeons, fellows and residents that are current members of a participating orthopaedic society. We have developed a unique questionnaire to gather information concerning gender-based and sexual harassment experiences of female orthopaedic surgeons, fellows and residents throughout their education and current practices. The questionnaire will be administered online using SurveyMonkey®, the online survey tool, to ensure anonymity. Reminder emails will be distributed up to two times after the survey is initially distributed to maximize the number of responses and, thus, validity and generalizability. Descriptive analyses and multivariable logistic regression analyses will be conducted to analyze the collected data. Conclusions. The results of this study are likely to bring to light a critical issue in orthopaedic surgery and will hopefully provide the impetus for orthopaedic departments and societies to develop and enforce policies that limit these destructive behaviors in the workplace. We hope the results will provide sufficient information to determine if these experiences are one of the factors leading to the pronounced gender disparity within this field. Ethics and Dissemination. An ethics application is currently under review with the Hamilton Integrated Research Ethics Board (HiREB) in Hamilton, ON, Canada. The results of this initiative will be disseminated through peer-reviewed publications and conference presentations.
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 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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.026 |
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".