Fathers' Leave, Fathers' Involvement and Child Development
Bibliographic record
Abstract
Previous research has shown that fathers taking some time off work around childbirth, especially periods of leave of 2 or more weeks, are more likely to be involved in childcare related activities than fathers who do not do so. Furthermore, evidence suggests that children with fathers who are ‘more involved’ perform better during the early years than their peers with less involved fathers. This paper analyses data of four OECD countries — Australia; Denmark; United Kingdom; United States — to describe how leave policies may influence father’s behaviours when children are young and whether their involvement translates into positive child cognitive and behavioural outcomes. This analysis shows that fathers’ leave, father’s involvement and child development are related. Fathers who take leave, especially those taking two weeks or more, are more likely to carry out childcare related activities when children are young. This study finds some evidence that children with highly involved fathers tend to perform better in terms of cognitive test scores. Evidence on the association between fathers’ involvement and behavioural outcomes was however weak. When data on different types of childcare activities was available, results suggest that the kind of involvement matters. These results suggest that what matters is the quality and not the quantity of father-child interactions.
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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 | 0.002 |
| 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 | 0.001 |
| 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".