Gender‐Based Microaggressions in Surgery: A Scoping Review of the Global Literature
Bibliographic record
Abstract
BACKGROUND: In addition to systemic gender disparities, women in surgery encounter interpersonal microaggressions. The objective of this study is to describe the most common forms of microaggressions reported by women in surgery. METHODS: We conducted a scoping review using PubMed/MEDLINE, Ovid, and Web of Science to describe the international, indexed English-language literature on gender-based microaggressions experienced by female surgeons, surgical trainees, and medical students in surgery. After screening by title, abstract, and full-text, 37 articles were retained for data extraction and analysis. Microaggressions were analyzed using the Sexist Microaggression Experience and Stress Scale (MESS) framework and stratified by country of origin. RESULTS: Gender-based microaggression publications most commonly originated from the United States (n = 27 articles), Canada (n = 3), and India (n = 2). Gender-based microaggressions were classified into environmental invalidations (n = 20), being treated like a second-class citizen (n = 18), assumptions of traditional gender roles (n = 12), sexual objectification (n = 11), assumptions of inferiority (n = 10), being forced to leave gender at the door (n = 8), and experiencing sexist language (n = 6). Additionally, attendings were more frequently reported to experience microaggressions than surgical trainees and medical students, but more articles reported data on attendings (n = 16) than surgical trainees (n = 10) or students (n = 4). CONCLUSION: While recent advancements have opened the field of surgery to women, there is still a lack of female representation, and persistent microaggressions may perpetuate this gender disparity. Addressing microaggressions against female surgeons is essential to achieving gender equity in surgical practice.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".