Does extending the duration of legislated paid maternity leave improve breastfeeding practices? Evidence from 38 low-income and middle-income countries
Bibliographic record
Abstract
INTRODUCTION: Among all barriers to breastfeeding, the need to work has been cited as one of the top reasons for not breastfeeding overall and for early weaning among mothers who seek to breastfeed. We aimed to examine whether extending the duration of paid maternity leave available to new mothers affected early initiation of breastfeeding, exclusive breastfeeding under 6 months and breastfeeding duration in low-income and middle-income countries (LMICs). METHODS: We merged longitudinal data measuring national maternity leave policies with information on breastfeeding related to 992 419 live births occurring between 1996 and 2014 in 38 LMICs that participated in the Demographic and Health Surveys. We used a difference-in-differences approach to compare changes in the prevalence of early initiation and exclusive breastfeeding, as well as the duration of breastfeeding, among treated countries that lengthened their paid maternity leave policy between 1995 and 2013 versus control countries that did not. Regression models included country and year fixed effects, as well as measured individual-level, household-level and country-level covariates. All models incorporated robust SEs and respondent-level sampling weights. RESULTS: A 1-month increase in the legislated duration of paid maternity leave was associated with a 7.4 percentage point increase (95% CI 3.2 to 11.7) in the prevalence of early initiation of breastfeeding, a 5.9 percentage point increase (95% CI 2.0 to 9.8) in the prevalence of exclusive breastfeeding and a 2.2- month increase (95% CI 1.1 to 3.4) in breastfeeding duration. CONCLUSION: Extending the duration of legislated paid maternity leave appears to promote breastfeeding practices in LMICs. Our findings suggest a potential mechanism to reduce barriers to breastfeeding for working mothers.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".