Why Is the U.S. Preterm Birth Rate So Much Higher Than the Rates in Canada, Great Britain, and Western Europe?
Bibliographic record
Abstract
The portion of newborns delivered before term is considerably higher in the United States than in other developed countries. We compare the array of risk exposures and protective factors common to women across national settings, using national, regional, and international databases, review articles, and research reports. We find that U.S. women have higher rates of obesity, heart disease, and poor health status than women in other countries. This is in part because more U.S. women are exposed to the stresses of racism and income disparity than women in other national settings, and stress loads are known to disrupt physiological functions. Pregnant women in the United States are not at higher risk for preterm birth because of older maternal age or engagement in high-risk behaviors. However, to a greater extent than in other national settings, they are younger and their pregnancies are unintended. Higher rates of multiple gestation pregnancies, possibly related to assisted reproduction, are also a factor in higher preterm birth rates. Reproductive policies that support intentional childbearing and social welfare policies that reduce the stress of income insecurity can be modeled from those in place in other national settings to address at least some of the elevated U.S. preterm birth rate.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".