Gender disparity in delayed childbearing among medical trainees in Ontario
Bibliographic record
Abstract
Objective: Physicians report high rates of delayed childbearing and are at increased risk of infertility and pregnancy loss. There are limited studies on this topic in the Canadian context, particularly for trainees. Our objective was to explore Ontario medical trainees' experiences with and knowledge of delayed childbearing, infertility, and fertility treatments. Methods: We administered a cross-sectional survey to all residents and fellows in Ontario. Descriptive statistics, multiple regression, and thematic analysis of free text responses are used to present the findings. Results: 460 trainees responded to the survey. Over half (57%) intentionally delayed childbearing due to medical training, with long working hours being the most cited reason (82%). Cis women were 85% more likely to delay family initiation than cis men. Rates of early pregnancy loss (17%) were similar to that of the Canadian average for this age group, while rates of infertility (14%) were slightly higher. Knowledge gaps were identified, with trainees scoring 62% on knowledge questions around age-related fertility decline and fertility treatment. The majority (73%) felt their programs were supportive of family initiation during training, with top areas for change identified as increased flexibility with working hours, and increased protected time for required extracurricular activities. Conclusion: Trainee physicians in Ontario report high rates of delaying family initiation due to training, with greater impacts on cis women compared to cis men, and slightly higher rates of infertility. Addressing knowledge gaps is one way to empower trainees to make informed family planning decisions going forward.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".