A Canadian National Survey Study of Harassment in Surgery—Still a Long Way to Go
Bibliographic record
Abstract
OBJECTIVES: Previous literature has consistently documented harassment and discrimination in surgery. These experiences may contribute to the continuing gender inequity in surgical fields. The objective of our study was to survey Canadian surgeons and surgical trainees to gain a greater understanding of the experience of harassment across genders, career stage, and specialty. METHODS: A cross-sectional, online survey was distributed to Canadian residents, fellows, and practicing surgeons in general surgery, plastic surgery, and neurosurgery through their national society email lists and via social media posts. RESULTS: There were 194 included survey respondents (60 residents, 11 fellows, and 123 staff) from general surgery (44.8%), plastic surgery (42.7%), and neurosurgery (12.5%). 59.8% of women reported having experienced harassment compared to only 26.0% of men. Women were significantly more likely to be harassed by colleagues and patients/families compared to men. Residents (62.5%) were two times more likely to report being harassed compared to fellows/staff (38.3%). Residents were significantly more likely to be harassed by patients/families while fellows/staff were more likely to be harassed by colleagues. There were no significant differences in self-reported harassment across the three surgical specialties. There was no significant difference in rates of reported harassment between current residents (62.5%), and fellow/staff recollections of their experiences of harassment during residency (59.2%). CONCLUSIONS: The prevalence of gender-based discrimination remains high and harassment prevalence remains largely unchanged from when current staff were in residency. Our findings highlight a need to implement systemic changes to support the increasing number of women entering surgery, and to improve surgical culture to continue to attract the best and brightest to the field.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".