Loneliness and Psychological Distress Among Older Californians: The Moderating Roles of Citizenship Status and English Proficiency
Bibliographic record
Abstract
OBJECTIVES: Guided by the theory of stress proliferation, our study examined whether loneliness, citizenship status, and English proficiency were associated with psychological distress among older adults, and if citizenship status and English proficiency moderated these relationships. METHODS: Using the older adult subsample (65+ years) of the 2019-2020 California Health Interview Survey (N = 15,210), we assessed cross-sectional associations between loneliness, citizenship status, and English proficiency on psychological distress by conducting multivariable linear regression models. Interaction terms were included in subsequent models to determine if citizenship status and English proficiency moderated the relationship between loneliness and psychological distress. RESULTS: In unadjusted models, greater loneliness was associated with higher distress. Both naturalized citizens and noncitizens, and those with limited English proficiency (LEP) exhibited greater distress than US born citizens and those who speak English only (EO). After adjusting for sociodemographic and health covariables, loneliness remained significant for distress although the relationships between citizenship status and English proficiency became attenuated. With the inclusion of interactions, the magnitude of the relationship between loneliness and distress was stronger for naturalized citizens and those with LEP than native-born citizens and those who speak EO, respectively. DISCUSSION: Loneliness was the most consistent stressor affecting multiple life domains. However, our findings demonstrate that stress proliferation is occurring among older immigrant adults and the interplay between loneliness, citizenship status, and English proficiency is contributing to heightened distress. Further attention is needed in understanding the role of multiple stressors influencing mental health among immigrant 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".