RACIAL DIFFERENCES IN LONGITUDINAL TRAJECTORIES OF SOCIAL ISOLATION AND LONELINESS
Bibliographic record
Abstract
Abstract The purpose of this study was to determine if there are racial differences in the longitudinal trajectories of social isolation and loneliness among older adults. Data come from the Health and Retirement Study Leave Behind Questionnaire, waves 2006-2016. Social isolation was operationalized as a social network index and loneliness was operationalized by the UCLA-3 item loneliness scale. Race was operationalized by White, Black, and Hispanic. I also adjusted for age, gender, education, household income, and employment status. Racial differences in longitudinal trajectories were determined by multilevel models with survey weights included by the HRS. In unadjusted models, I found Hispanic older adults were less likely to be socially isolated compared to White and Black older adults; furthermore, White older adults were less likely to be lonely compared to Black and Hispanic older adults. In fully adjusted models, White older adults had greater social isolation in comparison to Black and Hispanic older adults, and Black older adults had greater isolation compared to Hispanic older adults. For loneliness, Black older adults were found to be lonelier compared to Hispanic older adults, but not White older adults. This study is one of the first to examine racial differences in longitudinal trajectories of social isolation and loneliness. I find race is a salient factor that may influence objective isolation and feelings of loneliness among older adults. Given the established relationship between isolation, loneliness, and health, these findings are useful for identifying potential racial groups at higher risk for these conditions and informing future interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".