Returning to Surgical Residency After Maternity Leave: Exploring Challenges and Shining a Light on the Motherhood Advantage
Bibliographic record
Abstract
OBJECTIVE: Surgeon and surgical resident mothers continue to face discrimination and inequities related to motherhood. Current literature reports on the logistical challenges and some of the negative perceptions faced by these individuals, but we do not know the whole story of how women navigate these challenges. With increasing attention to EDI, understanding women's experiences is crucial for transforming surgical culture. DESIGN: Using constructivist grounded theory, the experiences of surgical residents as mothers were explored as they reflected on their transition back to work after maternity leave. Semi-structured interviews were conversational, and interview questions evolved throughout the study. Interview transcripts were analyzed using constant comparative analysis. SETTING: This was a Canadian-wide study, including current trainee or faculty participants who had completed surgical training at Canadian institutions. PARTICIPANTS: Eligible participants were any faculty surgeon or current trainee who had taken a maternity leave from a Canadian surgical program between 2012 and 2022. Twenty-one participants were included from the specialties of general surgery, plastic surgery, vascular surgery, orthopedics, urology, ophthalmology, and obstetrics and gynecology. RESULTS: Participants detailed the challenges they faced returning to surgical residency after maternity leave, and the factors that made this transition easier. Upon reflection, participants described this transition as transformative for their professional identity, highlighting how motherhood facilitated the development of essential skills including communication, empathy, and setting boundaries. CONCLUSIONS: Despite efforts to better support women in surgery, challenges with the transition back to work following maternity leave persist. Both individuals and systems need to reevaluate priorities, design more flexible call schedules, implement better accommodations for chest-feeding parents, and establish mentorship programs for all parents. Negative perceptions of and biases against surgeon mothers must also be challenged. Notably, this study's novel findings that motherhood enhances, rather than detracts from, surgical skill development should be recognized.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".