Socioeconomic Inequalities in Low Birth Weight in the United States, the United Kingdom, Canada, and Australia
Bibliographic record
Abstract
OBJECTIVES: To compare associations between socioeconomic status and low birth weight across the United States, the United Kingdom, Canada, and Australia, countries that share cultural features but differ in terms of public support and health care systems. METHODS: Using nationally representative data from the United States (n = 8400), the United Kingdom (n = 12 018), Canada (n = 5350), and Australia (n = 3452) from the early 2000s, we calculated weighted prevalence rates and adjusted odds of low birth weight by income quintile and maternal education. RESULTS: Socioeconomic gradients in low birth weight were apparent in all 4 countries, but the magnitudes and patterns differed across countries. A clear graded association between income quintile and low birth weight was apparent in the United States. The relevant distinction in the United Kingdom appeared to be between low, middle, and high incomes, and the distinction in Canada and Australia appeared to be between mothers in the lowest income quintile and higher-income mothers. CONCLUSIONS: Socioeconomic inequalities in low birth weight were larger in the United States than the other countries, suggesting that the more generous social safety nets and health care systems in the United Kingdom, Canada, and Australia played buffering roles.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".