Exploring the influence of loneliness on cognitive decline: differential effects in Black and White older adults
Bibliographic record
Abstract
OBJECTIVES: Loneliness is prevalent among older adults, who may experience age-related or pathological cognitive changes. However, there's a limited understanding of how loneliness affects cognitive decline and if the relationship differs by race. This study investigates the impact of loneliness on cognitive decline among Black and White older adults. METHODS: 3,082 participants with 20,882 follow-ups over 10 years were included from the RUSH RADC cohorts. Participants were categorized as having high or low loneliness based on a median split of de Jong-Gierveld Loneliness Scale scores. Participants were divided into four groups based on race (White vs. Black) and loneliness status (high vs. low): (1) White-high (n = 1,030), (2) White-low (n = 913), (3) Black-high (n = 493), (4) Black-low (n = 646). Linear mixed-effects models were employed to examine group differences in cognitive change over time (global cognition, episodic memory, semantic memory, perceptual speed, visuospatial ability, and working memory). RESULTS: White adults with high loneliness exhibited increased rates of cognitive decline across all domains (p < .01) compared to Black adults with high or low. Compared to White adults with low loneliness, White adults with high loneliness exhibited increased decline in all domains except perceptual speed and visuospatial ability (p < .001). Black adults with high loneliness exhibited increased decline compared to Black adults with low loneliness in all domains (p < .01). DISCUSSION: Loneliness is significantly associated with cognitive decline. Results of this study suggest that the loneliness-cognition relationship is stronger in White adults compared to Black older adults.
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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 | 0.003 |
| 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 | 0.001 |
| 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".