The Association between Environmental Factors, Race, and Cognitive Status
Bibliographic record
Abstract
Abstract Based on the data from National Social Life, Health and Aging Project, Wave 3, this study examined two research questions: what is the role of race in predicting cognitive status? and what are predictors of cognitive status between white and black older adults? Cognitive status was assessed using the 18-item survey-adapted Montreal Cognitive Assessment. Using the ecological framework, correlates of cognitive status were conceptualized in three levels of environments: micro- (personal health), meso- (social relationship), and macro-environments (community characteristics). Hierarchical regressions analyses were employed. Findings indicated that 83% of the sample (n= 2,829) were whites and the mean age was 72.95. Bivariate analyses suggested significant racial differences in cognitive status, marital status, income, education, health, social relationship, and community characteristics. Additive and interactive models showed that race had an independent effect as well as joint effects with the three levels of environments in explaining cognitive status. Parallel regression analyses for each racial group were undertaken and models were significant (P < .0001). In two separate models, common predictors for better cognition included being younger, more educated, fewer IADL impairments, and less depression. For older whites, unique correlates for better cognition were being female, higher income, sense of control in life, safer community, and neighbor relations. The only unique correlate for older blacks to have better cognition was community cohesion. Results provided insights on racial differences in cognition experienced among community-dwelling older Americans, and emphasized the need for social programs that promote race-sensitive, age-friendly communities to protect against cognitive decline.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".