Parental-leave policies and perceptions of pregnancy during surgical residency training in North America: a scoping review
Bibliographic record
Abstract
BACKGROUND: The number of surgical residents experiencing childbearing during residency training is increasing, and there is an absence of clarity with respect to parental-leave, lactation and return-to-work policies in support of residents. The aim of this review was to examine parental-leave policies during residency training in surgery and the perceptions of these policies by residents, program directors and coresidents, as described in the literature. METHODS: We performed a scoping review of the literature based on the following themes: maternity or parental-leave policies; antepartum work-restriction policies and obstetric complications; accommodations for training absences; support for, and perceptions of, maternity or parental leave during residency training; and challenges upon return to work, namely resident performance and breastfeeding. RESULTS: Parental-leave policies during surgical residency training have historically lacked clarity and enforcement. Although recommendations for parental leave are now in place, this may have historically contributed to a lack of perceived support for surgical residents and may result in variable leave permitted to residents. Unclear policies may also contribute to career dissatisfaction among resident parents, which may deter qualified individuals from selecting surgical subspecialties. CONCLUSION: A call for a cultural shift is required to inform policies that would better support residents across all surgical specialties to pursue success in their dual roles as parents and surgeons. With increased awareness, progress in policy and guideline development is under way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".