Comparison of Male and Female Surgeons’ Experiences With Gender Across 5 Qualitative/Quantitative Domains
Bibliographic record
Abstract
Importance: A growing body of literature has been developed with the goal of attempting to understand the experiences of female surgeons. While it has helped to address inequities and promote important programmatic improvements, work remains to be done. Objective: To explore how practicing male and female surgeons' experiences with gender compare across 5 qualitative/quantitative domains: career aspirations, gender-based discrimination, mentor-mentee relationships, perceived barriers, and recommendations for change. Design, Setting, and Participants: This national concurrent mixed-methods survey of Fellows of the American College of Surgeons (FACS) compared differences between male and female FACS. Differences between female FACS and female members of the Association of Women Surgeons (AWS) were also explored. A randomly selected 3:1 sample of US-based male and female FACS was surveyed between January and June 2020. Female AWS members were surveyed in May 2020. Exposure: Self-reported gender. Main Outcomes and Measures: Self-reported experiences with career aspirations (quantitative), gender-based discrimination (quantitative), mentor-mentee relationships (quantitative), perceived barriers (qualitative), and recommendations for change (qualitative). Results: A total of 2860 male FACS (response rate: 38.1% [2860 of 7500]) and 1070 female FACS (response rate: 42.8% [1070 of 2500]) were included, in addition to 536 female AWS members. Demographic characteristics were similar between randomly selected male and female FACS, with the notable exception that female FACS were less likely to be married (720 [67.3%] vs 2561 [89.5%]; nonresponse-weighted P < .001) and have children (660 [61.7%] vs 2600 [90.9%]; P < .001). Compared with female FACS, female AWS members were more likely to be younger and hold additional graduate degrees (320 [59.7%] were married; 238 [44.4%] had children). FACS of both genders acknowledged positive and negative aspects of dealing with gender in a professional setting, including shared experiences of gender-based harassment, discrimination, and blame. Female FACS were less likely to have had gender-concordant mentors. They were more likely to emphasize the importance of gender when determining career aspirations and prioritizing future mentor-mentee relationships. Moving forward, female FACS emphasized the importance of avoiding competition among female surgeons. They encouraged male surgeons to acknowledge gender bias and admit their potential role. Male FACS encouraged male and female surgeons to treat everyone the same. Conclusions and Relevance: Experiences with gender are not limited to supportive female surgeons. The results of this study emphasize the importance of recognizing the voices of all stakeholders involved when striving to promote workforce diversity and the related need to develop quality improvement/surgical education initiatives that enhance inclusion through open, honest discourse.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".