RACIAL DIFFERENCES IN THE RELATIONSHIP BETWEEN LONELINESS AND COGNITION AMONG OLDER ADULTS IN THE MIDWEST
Bibliographic record
Abstract
Abstract Findings from various studies have revealed a relationship between loneliness and negative health outcomes, including cognitive decline and dementia. The strength and direction of this relationship has been contested as there is wide variability in definitions and testing criteria for loneliness. If loneliness is risk factor for cognitive decline, it may represent a cost-effective site for interventional design. We used data from the Precision Health Initiative’s Person to Person Health Interview Study (P2P), a cross-sectional survey conducted in Indiana from 2020-2021, to investigate the relationship between loneliness (UCLA 3-item loneliness scale) and cognition (The Montreal Cognitive Assessment; MoCA) among older adults and to determine if the strength of the relationship varies for black and white adults. Among our subsample of adults 55 and older, over one quarter (26.7%) reported loneliness, with white respondents reporting more loneliness than black respondents (26.8 and 22.0%, respectively). Being lonely was associated with lower cognition, as was being older, male, black, and having no college education. However, we found that loneliness was associated with worse cognition, for white adults only. Although black respondents in our sample reported more loneliness than older white adults after age 70, we did not have adequate power to determine if advanced age moderated the relationship. Our findings highlight the role of loneliness in cognition for older, white adults and the need for more research to assess this relationship for the “mid-“ and “oldest-old” black adults who may be more susceptible to loneliness due, in part, to racial disparities in mortality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".