The role of gender in the decision to pursue a surgical career: A qualitative, interview-based study
Bibliographic record
Abstract
BACKGROUND: Previous literature has explored the underrepresentation of women in surgery. However, this research has often been quantitative or limited by considering only the perspectives and experiences of women at more advanced career stages. Here, we use a qualitative methodology and a sample of women and men across the career continuum to identify the role that gender plays in the decision to pursue a surgical career. METHODS: We audio-recorded and transcribed semi-structured interviews conducted with 12 women and 12 men ranging in their level of medical training from medical students to residents to staff surgeons. We used Braun and Clarke's six-step approach to thematic analysis to analyze the data, maintaining trustworthiness and credibility by employing strategies including reflexivity and participant input. RESULTS: Our findings suggested that the characteristics of surgery and early exposure to the profession served as important factors in participants' decisions to pursue a surgical career. Although not explicitly mentioned by participants, each of these areas may implicitly be gendered. Gender-based factors explicitly mentioned by participants included the surgical lifestyle and experiences with gender discrimination, including sexual harassment. These factors were perceived as challenges that disproportionately affected women and needed to be overcome when pursuing a surgical career. CONCLUSIONS: Our findings suggest that gender is more likely to act as a barrier to a career in surgery than as a motivator, especially among women. This suggests a need for early experiences in the operating room and mentorship. Policy change promoting work-life integration and education to target gender discrimination is also recommended.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".