Surgical trainees’ experience of pregnancy, maternity and paternity leave: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Internationally, supporting surgical trainees during pregnancy, maternity and paternity leave is essential for trainee well-being and for retention of high-calibre surgeons, regardless of their parental status. This study sought to determine the current experience of surgical trainees regarding pregnancy, maternity and paternity leave. METHODS: A cross-sectional anonymised electronic voluntary survey of all surgical trainees working in the UK and Ireland was distributed via the Association of Surgeons in Training and the British Orthopaedic Trainees' Association. RESULTS: There were 876 complete responses, of whom 61.4% (n=555) were female. 46.5% (258/555) had been pregnant during surgical training. The majority (51.9%, n=134/258) stopped night on-call shifts by 30 weeks' gestation. The most common reason for this was concerns related to tiredness and maternal health. 41% did not have rest facilities available on night shifts. 27.1% (n=70/258) of trainees did not feel supported by their department during pregnancy, and 17.1% (n=50/258) found the process of arranging maternity leave difficult or very difficult. 61% (n=118/193) of trainees felt they had returned to their normal level of working within 6 months of returning to work after maternity leave, while a significant minority took longer. 25% (n=33/135) of trainees found arranging paternity leave difficult or very difficult, and the most common source of information regarding paternity leave was other trainees. CONCLUSION: Over a quarter of surgical trainees felt unsupported by their department during pregnancy, while a quarter of male trainees experience difficulty in arranging paternity leave. Efforts must be made to ensure support is available in pregnancy and maternity/paternity leave.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".