Abstract: Sexual Inequality for Women in Plastic Surgery: A Systematic Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Previous research has highlighted the gender-based disparities that are present throughout the field of surgery.1 The aim of this study is to evaluate the breadth and variability of the issues facing women in plastic surgery, worldwide. METHODS: A systematic scoping review was undertaken from October 2016 to January 2017, with no restrictions on date or language. We followed the five scoping review steps as proposed by Arskey and O’Malley: (1) Identification of the research question; (2) Identification of relevant studies; (3) Study selection; (4) Data charting and (5) Collation and reporting of results.2 A narrative synthesis of the literature according to themed issues was developed, together with a summary of relevant numeric data. RESULTS: From the 2,247 articles found in the search, a total of 53 articles were included in the final analysis. The majority of articles were published from the US. 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 10. Retirement and financial planning. DISCUSSION AND CONCLUSION: There were several key findings. First, 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%-25.7%.3,4 Academic plastic surgeons are less frequently female than male, and women academic plastic surgeons score less favorably when outcomes of academic success, such as h index and number of peer-reviewed publications are evaluated.5 Finally, there has been a shift away from overt discrimination towards a more ingrained, implicit bias affecting individuals and institutions; most published cases of bias and discrimination are in association with pregnancy. 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. Reference Citations: 1. Kawase K, Carpelan-holmstrom M, Kwong A, Sanfey H. Factors that Can Promote or Impede the Advancement of Women as Leaders in Surgery: Results from an International Survey. World J Surg. 2016;40:258–66. 2. Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19–32. 3. Aspalter M, Linni K, Metzger P, Hölzenbein T. Female choice for surgical specialties: development in Germany, Austria, and Switzerland over the past decade. Eur Surg. 2014;46:234–8. 4. Macadam SA, Kennedy S, Lalonde D, Anzarut A, Clarke HM, Brown EE. The Canadian plastic surgery workforce survey: interpretation and implications. Plast Reconstr Surg. 2007;119:2299–306. 5. Therattil PJ, Hoppe IC, Granick MS, Lee ES. Application of the h-Index in Academic Plastic Surgery. Ann Plast Surg. 2014;0:1.
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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 | 0.020 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".