Paid parental leave and family wellbeing in the sustainable development era
Bibliographic record
Abstract
BACKGROUND: The Sustainable development goals (SDGs) have the potential to have a significant impact on maternal and child health through their commitments both to directly addressing health services and to improving factors that form the foundation of social determinants of health. To achieve change at scale, national laws and policies have a critical role to play in implementing the SDGs' commitments. One particular policy that could advance a range of SDGs and importantly improve maternal and infant health is paid parental leave. METHODS: This article analyzes literature on paid leave and related policies relevant to SDG 1 (poverty), SDG 3 (health), SDG 5 (gender equality), SDG 8 (decent work), and SDG 10 (inequality). In addition, this article presents global data on the prevalence of policies in all 193 UN Member States. RESULTS: A review of the literature finds that paid parental leave may support improvements across a range of SDG outcomes relevant to maternal and child health. Across national income levels, paid leave has been associated with lower infant mortality and higher rates of immunizations. In high-income countries, studies have found that paid leave increases exclusive breastfeeding and may improve women's economic outcomes. However, factors including the duration of leave, the wage replacement rate, and whether leave is made available to both parents importantly shape the impacts of paid leave policies. While most countries now offer at least some paid maternal leave, many provide less than the 6 months recommended for exclusive breastfeeding, and only around half as many provide paternal leave. CONCLUSIONS: To accelerate progress on the SDGs' commitments to maternal and child health, we should monitor countries' actions on enacting or strengthening paid leave policies. Further research is needed on the duration, wage replacement rate, and availability of leave before and after birth that would best support both child and parental health outcomes and social determinants of health more broadly. In addition, further work is needed to understand the extent to which paid leave policies extend to the informal economy, where the majority of women and men in low- and middle-income countries work.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".