Work-Related Factors and Pregnancy Outcomes in Female Surgeons
Bibliographic record
Abstract
Objective: To describe work-related factors, pregnancy, and pregnancy outcomes in female surgeons is the objective of this study. Background: Some data suggest surgeon workload may deter pregnancy and adversely affect pregnancy outcomes in female surgeons. Methods: A cross-sectional, web-based survey was distributed via e-mail to members of the Society of Obstetrics and Gynaecologists of Canada and to surgical departments of 6 Canadian universities from October 2019 to January 2020. Results: A total of 223 surgeons with 451 pregnancies participated. Work hours were reduced in 33.3% of pregnancies, and 28.0% had a policy for pregnancy in their workplace. A total of 57% of surgeons intentionally delayed pregnancy due to heavy workload and 39% to career concerns, and 31% reported work adversely affected their pregnancy. Adverse maternal outcomes included miscarriage (14.9%), preterm labor (6.2%), hypertension (5.5%), pre-eclampsia (2.9%), and placenta praevia (1.3%). Adverse infant outcomes included preterm birth (6.9%), small for gestational age at birth (6.9%), and neonatal intensive care unit admission (4%). Congenital anomalies occurred in 4.2% of pregnancies. Surgeons who reported a policy for working while pregnant were more likely to have reduced their work hours than those without a policy (48.4% vs 28.5% respectively, P < 0.0001). In unadjusted models, those who reduced their work hours while pregnant were less likely to have a miscarriage than those who did not (odds ratio = 0.2, 95% confidence interval, 0.1–0.4). Conclusions: Female surgeons reported delays in pregnancy due to work, adverse effects of work on pregnancy, and some elevated rates of adverse outcomes. These data support policies for pregnancy in surgeons and surgical trainees.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".