Reproductive patterns, pregnancy outcomes and parental leave practices of women physicians in Ontario, Canada: the Dr Mom Cohort Study protocol
Bibliographic record
Abstract
INTRODUCTION: Surveys and qualitative studies suggest that women physicians may delay childbearing, be at increased risk of adverse peripartum complications when they do become pregnant, and face discrimination and lower earnings as a result of parenthood. Observational studies enrolling large, representative samples of women physicians are needed to accurately evaluate their reproductive patterns, pregnancy outcomes, parental leave practices and earnings. This protocol provides a detailed research plan for such studies. METHODS AND ANALYSIS: The Dr Mom Cohort Study encompasses a series of retrospective observational studies of women physicians in Ontario, Canada. All practising physicians in Ontario are registered with the College of Physicians and Surgeons of Ontario (CPSO). By linking a dataset of physicians from the CPSO to existing provincial administrative databases, which hold health data and physician billing records, we will be able to retrospectively assess the healthcare utilisation, work practices and pregnancy outcomes of women physicians at the population level. Specific outcomes of interest include: (1) rates and timing of pregnancy; (2) pregnancy-related care and complications; and (3) duration of parental leave and subsequent earnings, each of which will be evaluated with regression methods appropriate to the form of the outcome. We estimate that, at minimum, 5000 women physicians will be eligible for inclusion. ETHICS AND DISSEMINATION: This protocol has been approved by the Research Ethics Board at St. Michael's Hospital in Toronto, Ontario, Canada (#18-248). We will disseminate findings through several peer-reviewed publications, presentations at national and international meetings, and engagement of physicians, residency programmes, department heads and medical societies.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".