Individual, family, and employer: Factors associated with fathers' use of parental leave
Bibliographic record
Abstract
Abstract Objective This study aims to examine factors associated with fathers' use of parental leave in Canada, considering a rich set of individual, family, and employer characteristics. Background The province of Quebec and the rest of Canada have two different parental benefits programs and show different patterns in fathers' parental benefit use. Also, the role of employers in fathers' benefit use has gained little attention in the Canadian context. Method Using Canadian administrative data, logistic regression models were estimated separately for the two regions to examine characteristics associated with the likelihood of using parental benefits among fathers whose first child was born in 2016. Results The percentage of male coworkers who used parental benefits, employer's industry, fathers' earnings, and whether the mother received parental benefits were important factors for fathers' parental benefit use in both Quebec and the rest of Canada. Some of these associations were opposite in the two regions. Conclusion Not only individual and family characteristics but also employer characteristics are important for understanding fathers' parental benefit use, and these associations depend on the design of a parental benefit program. Implications Findings can be used to improve a parental benefit program to target fathers with low uptake or their employers.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".