Gender Inequality for Women in Plastic Surgery: A Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: Previous research has highlighted the gender-based disparities present throughout the field of surgery. This study aims to evaluate the breadth of the issues facing women in plastic surgery, worldwide.METHODS: A systematic scoping review was undertaken from October of 2016 to January of 2017, with no restrictions on date or language. A narrative synthesis of the literature according to themed issues was developed, together with a summary of relevant numeric data.RESULTS: From the 2247 articles identified, 55 articles were included in the analysis. The majority of articles were published from the United States. Eight themes were identified, as follows: (1) workforce figures; (2) gender bias and discrimination; (3) leadership and academia; (4) mentorship and role models; (5) pregnancy, parenting, and childcare; (6) relationships, work-life balance, and professional satisfaction; (7) patient/public preference; and (8) retirement and financial planning. Despite improvement in numbers over time, women plastic surgeons continue to be underrepresented in the United States, Canada, and Europe, with prevalence ranging from 14 to 25.7 percent. Academic plastic surgeons are less frequently female than male, and women academic plastic surgeons score less favorably when outcomes of academic success are evaluated. Finally, there has been a shift away from overt discrimination toward a more ingrained, implicit bias, and most published cases of bias and discrimination are in association with pregnancy.CONCLUSIONS: The first step toward addressing the issues facing women plastic surgeons is recognition and articulation of the issues. Further research may focus on analyzing geographic variation in the issues and developing appropriate interventions.
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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 | 0.002 | 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) | 0.000 | 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".