Pregnancy and Parenthood Remain Challenging During Surgical Residency: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To examine common themes and synthesize data surrounding pregnancy and parenthood during surgical residency training. METHOD: The authors conducted a systematic search of the literature in March 2019. They searched MEDLINE, EMBASE, and Scopus, seeking articles published from 2003 to 2018 that focused on pregnancy, parenthood, and the experience of surgical residents. They excluded articles that examined nonsurgical programs, as well as editorials, abstracts, and commentaries. Two investigators independently reviewed all citations, selected articles for full-text review, and extracted data from the selected articles. RESULTS: Of 523 titles and abstracts screened, 27 were included. Overall, female surgical residents had fewer children during residency training than their male counterparts (18%-28% vs 32%-54%). As compared with the general population, surgical residents had their first child later in life (30-34 vs 25 years old), and had fewer children overall (0.6-2.1 vs 2.7). Infertility rates were higher among female surgeons than in the general population (30%-32% vs 11%), as were assisted reproductive technology rates (8%-13% vs 1.7%). Pregnant surgical residents experienced a high rate of obstetrical complications; working more than 6 overnight calls shifts per month or 60 hours per week were predictors of increased complication rates. The authors noted no differences in attrition, caseload, or exam pass rates amongst female surgical residents who had become pregnant as compared with other residents. Despite these similar academic outcomes, negative attitudes and perceptions toward pregnancy during residency were consistently identified. CONCLUSIONS: Female surgical residents experience high rates of infertility and obstetrical complications, contend with negative attitudes and stigma during their pregnancies, and voluntarily delay childbearing. Formal maternity policies, a shift in surgical culture, and ongoing discussion with all stakeholders are needed to attract and retain female surgical residents.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".