RACIAL/ETHNIC DIFFERENCES IN LONELINESS AMONG OLDER ADULTS: THE ROLE OF INCOME AND EDUCATION AS MEDIATORS
Bibliographic record
Abstract
Abstract Our study examines differences in loneliness by race/ethnicity among older adults in the United States and whether income and education mediate these differences. Data came from the Health and Retirement Study Leave-Behind Questionnaire, 2014-2016. Loneliness was measured by the UCLA 3-item loneliness scale. Race/ethnicity categories were White, Black, and Hispanic/Latino. The mediator variables were total household income and education. Sociodemographic covariates included gender, age, employment status, living arrangements, country region, urbanicity, and HRS LBQ Wave. Multivariable linear regression models were used to determine differences in loneliness by race/ethnicity. The KHB mediation method was used to determine if income and education mediated racial/ethnic differences in loneliness. In bivariable analyses, White and Hispanic/Latinx older adults had comparable levels of loneliness, while Black older adults had higher loneliness scores. After multivariable adjustment, Hispanic/Latinx older adults had the lowest levels of loneliness while White and Black older adults had comparable levels of loneliness. A complete mediation was found between Whites and Blacks, in that income and education completely mediated differences in loneliness between these groups. A partial mediation was found between Whites and Hispanics, and between Blacks and Hispanics. Income as a mediator accounted for greater racial/ethnic differences in loneliness compared to education. Our study is the first to explicitly determine if socioeconomic factors mediate race/ethnicity differences in loneliness among a nationally representative sample of older adults. Study findings can inform evidence-based interventions and programs to reduce loneliness among 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".