Paternal Leave and Fathers’ Mental Health: A Rapid Literature Review
Bibliographic record
Abstract
Background: Several countries have introduced paternal leave policies in order to encourage and involve fathers in caregiving. Besides supporting fathers’ involvement, paternal leave may have other consequences such as health improvements. Paternity leave could potentially improve mental health outcomes by reducing stress and anxiety associated with work–family conflict. It can be hypothesized that paternal leave has a positive effect on men’s mental health; however, there have been no recent attempts to review the literature pertaining to such outcomes. Purpose: The aim of this rapid review of the literature was to evaluate the evidence from studies that explored the effect of paternal leave on men’s mental health. Design: Rapid literature review Methods: The review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases CINAHL Plus with Full Text, Academic Search Complete, MEDLINE, APA PsycArticles, APA PsycInfo, Social Sciences Full Text (H.W. Wilson), SocINDEX with Full Text, and ERIC were searched for studies that met the inclusion criteria. Findings: A total of 337 records were identified from the electronic database search. Nine studies met the inclusion criteria. The findings suggest that fathers experience mental health benefits as a result of availing of parental leave. The length of leave availed by fathers had an impact on their mental health, with longer duration of paternity leave associated with higher levels of mental well-being. Flexible leave impeded fathers from fully engaging in their paternal role or their employment duties. Conclusion: The mental health benefits of parental leave usually associated with mothers are also extended to fathers, highlighting the importance of this statutory entitlement.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".