Evidence on Maternal Health from Two Large Canadian Parental Leave Expansions: When is Enough Too Much?'
Bibliographic record
Abstract
Exploiting unique administrative longitudinal data sets on medical services provided to mothers before- and after- delivery, we estimate the causal effects of two major distinct parental leave reforms on maternal health outcomes, over a period of 5 years postpartum. The health outcomes are objective measures based on all types of medical services provided by physicians. For mothers publicly insured by the public prescription drug plan we can also identify all drugs used, in particular those associated with depressive symptoms. The long time span of the longitudinal administrative data sets allows an assessment of short-run and long-run effects of maternity leave on mothers? health. The empirical approach uses a strict regression discontinuity design based on the day of regime change. The large samples of mothers, who gave birth three months before and three months after the two policy changes (in 2001 and 2006), are drawn randomly from the population of delivering women, all covered by the universal public health care program. We do not find any evidence that the reforms had sizeable impacts on maternal health care costs, either of a physical or of a mental in nature, as measured by physicians? fee-for-service billing costs, prescription drug costs, or the number of hospitalizations. The second expansion has given rise to large fiscal costs over time as well as socioeconomic inequities.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".