A LIFE COURSE APPROACH TO NEIGHBORHOOD SEGREGATION AND HEALTH AMONG BLACK AND WHITE AMERICANS
Bibliographic record
Abstract
Abstract Research documents a positive association between high levels of neighborhood segregation and several negative health outcomes among Black Americans. However, few studies examine the relationship between neighborhood segregation and the health of both Black and White Americans. We apply a lifecourse framework to better understand how exposure to neighborhood segregation earlier in life may lead to racial inequities in health byway of cumulative advantages/disadvantages (CAD). Utilizing data from the nationally-representative Americans’ Changing Lives study (ACL), we employ growth curve models to investigate the relationship between neighborhood segregation in 1990 and the trajectories of two health outcomes over the adult lifespan: the number of chronic conditions and the probability of experiencing functional limitations. Our analysis reveals that, for Black participants, the baseline level of black segregation does not significantly impact their trajectories in these health outcomes. However, among White participants, residing in neighborhoods with lower levels of white segregation is associated with an increased probability of developing more chronic conditions and experiencing functional limitations later in life. We explore the roles of systemic and cultural racism as shaping forces in neighborhood structures and as contributors to negative health outcomes for both Black and White Americans. Research implications include further parsing different types of white underrepresentation in neighborhoods (i.e., living in predominantly Black neighborhoods vs racially diverse integrated areas) to examine their unique effects on health trajectories. Further, we discuss how intervention efforts may identify and address detractors from healthy aging for White Americans who are underrepresented in their neighborhoods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".