STRUCTURAL STRESSORS, PERCEIVED NEIGHBORHOOD CONTEXT, AND COGNITIVE FUNCTION IN BLACK MIDLIFE AND OLDER ADULTS
Bibliographic record
Abstract
Abstract Racial disparities in Alzheimer’s disease and related dementias have been established in the US, with higher rates in Black compared to White older adults. Because Black populations are disproportionately exposed to greater neighborhood disadvantage, chronic stress via the neighborhood context may contribute to worse cognitive health among Black older adults. However, the role of neighborhood-based stressors on cognitive function within the Black population is less understood. This study aims to examine how lifetime exposure to structural stressors (e.g., discrimination in housing, unemployment, and policing) and current perceived neighborhood stressors (e.g., high disorder and social discohesion) independently and interactively are associated with cognitive function in Black adults >50 years of age (n=1,663) using 2010-2016 Health and Retirement Study data. Linear regression models tested associations between structural stressors, perceived neighborhood stressors, and interaction terms of total number of structural stressors, neighborhood disorder (including safety and cleanliness), and social discohesion with cognitive function measured using the Langa-Weir 27-point scale. Black adults reported exposure to structural stressors as 15.69% in policing, 14.25% in hiring, and 7.88% in housing. In unadjusted models, greater total structural stressors were associated with greater cognitive function, while worse ratings of social cohesion, safety and cleanliness were associated with lower cognitive function. A statistically significant interaction term (b= -.221, SE=0.10, p<0.05) indicated that high social discohesion and greater total structural stressors was associated with lower cognitive function. Thus, life course exposure to multiple domains of neighborhood-based stressors may be important for Black adults’ cognitive function in later life.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".