Association of increased duration of legislated paid maternity leave with childhood diarrhoea prevalence in low-income and middle-income countries: difference-in-differences analysis
Bibliographic record
Abstract
Background Diarrhoea is the second-leading infectious cause of death in children younger than age 5 years. The global burden of severe diarrhoeal disease is concentrated in Africa and Southeast Asia, where a significant percentage of the population resides in low-resource settings. We aimed to quantitatively examine whether extending the duration of legislated paid maternity leave affected the prevalence of childhood diarrhoea in low-income and middle-income countries (LMICs). Methods We merged longitudinal data measuring national maternity leave policies with information on the prevalence of bloody diarrhoea related to 884 517 live births occurring between 1996 and 2014 in 40 LMICs that participated at least twice in the Demographic and Health Surveys between 2000 and 2015. We used a difference-in-differences approach to compare changes in the percentage of children with bloody diarrhoea across eight countries that lengthened their paid maternity leave policy between 1995 and 2013 to the 32 countries that did not. Results The prevalence of bloody diarrhoea in the past 2 weeks was 168 (SD=40) per 10 000 children under 5 years in countries that changed their policies and 136 (SD=15) in countries that did not. A 1-month increase in the legislated duration of paid maternity leave was associated with 61 fewer cases of bloody diarrhoea (95% CI −98.86 to −22.86) per 10 000 children under 5 years of age, representing a 36% relative reduction. Conclusion Extending the duration of paid maternity leave policy appears to reduce the prevalence of bloody diarrhoea in children under 5 years of age in LMICs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.002 |
| 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".