Creating and Using New Data Sources to Analyze the Relationship between Social Policy and Global Health: The Case of Maternal Leave
Bibliographic record
Abstract
OBJECTIVES: Operating at a societal level, public policy is often one of our best approaches to addressing social determinants of health (SDH). Yet, limited data availability has constrained past research on how national social policy choices affect health outcomes. We developed a new data infrastructure to illustrate how globally comparative data on labor policy might be used to examine the impact of social policy on health. METHODS: We used multivariate ordinary least squares regression models to examine the relationship between the duration of paid maternal leave and neonatal, infant, and child mortality rates in 141 countries when controlling for overall resources available to meet basic needs measured by per capita gross domestic product, total and government health expenditures, female literacy, and basic health care and public health provision. RESULTS: An increase of 10 full-time-equivalent weeks of paid maternal leave was associated with a 10% lower neonatal and infant mortality rate (p ≤ 0.001) and a 9% lower rate of mortality in children younger than 5 years of age (p ≤ 0.001). Paid maternal leave is associated with significantly lower neonatal, infant, and child mortality in non-Organisation for Economic Co-operation and Development (OECD) countries and OECD countries. CONCLUSIONS: This preliminary study, using newly available worldwide policy data, demonstrates the potential strength of using globally comparative data to examine SDH. Further data development to make multilevel modeling of the impact of labor conditions possible and to broaden which social policies can be examined is a critical next step.
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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 | 0.088 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".