Racial/ethnic minority segregation and low birth weight in five North American cities
Bibliographic record
Abstract
are rare in social epidemiology. Our prior work exploring racial/ethnic segregation and the prevalence of low birth weight (LBW) in communities from two large urban cities showed a strong relationship in Chicago and a very weak relationship in Toronto. This study extends that work by examining the association between racial/ethnic minority segregation and LBW in total of 307 communities in five North American cities: Baltimore, Boston, Chicago, Philadelphia, and Toronto. We used Pearson correlation coefficients and OLS regression models to examine potential variability in the association between racial/ethnic minority segregation and LBW, controlling for community-level unemployment. In a combined model with community-level data from all cities, a 10% increase in minority composition is associated with a 0.7% increase in LBW. While racial/ethnic minority segregation and unemployment are not associated with LBW in Toronto, these social determinants have strong and significant associations with LBW across communities in the four US cities in the analysis. Subsequent models revealed opposite effects for percentage non-Hispanic Black and percentage Hispanic. Across communities in the US cities in this analysis, there is considerable similarity in the strength of the effect of racial/ethnic segregation on LBW. Future work should incorporate communities from additional cities, looking to identify community assets and public policies that allow some minority communities to thrive, while other minority communities suffer from a high prevalence of LBW. More work is also needed on the generalizability of these patterns to other health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".