Investigating variations in paid parental leave uptake among mothers: a Canadian longitudinal population-based study
Bibliographic record
Abstract
This study examines how child, maternal, family, and health-related determinants contribute to variations in the uptake of paid maternal leave. We used data from the Quebec Longitudinal Study of Child Development, a representative cohort of infants born in 2020–2021 (N = 3456). Mothers were interviewed at 5 and 17 months postpartum, and three groups of leave were derived: no leave taking (n = 299), maternity leave with non-shared weeks of parental leave (n = 1927), and maternity leave with shared weeks of parental leave (n = 1150). Multivariate multinomial regression models using survey-weighted data yielded odds ratios. Low educational attainment, immigration background, poverty, cannabis use, and perinatal preventive services usage increased the odds of taking no leave, while being a first-time mother and a single parent, drinking alcohol during pregnancy, and accessing preventive withdrawal were associated with a decreased likelihood of not taking leave. Maternal age, lower educational attainment, and poverty were associated with increased odds, while immigration background and cannabis use were associated with decreased odds of taking maternity leave with non-shared (vs. shared) parental benefits. Mothers with no leave taking are more likely to experience increased socioeconomic hardship. Citizenship-based rather than employment-based parental leave policy could promote early-life equity across families from diverse backgrounds.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".