Stress internalization associated with cognitive decline among older U.S. Chinese
Bibliographic record
Abstract
Abstract INTRODUCTION Behavioral and sociocultural factors in minority populations in the U.S. are commonly examined as independent contributors or buffers to health disparities in cognitive decline, but can demonstrate significant inter-relatedness due to broader contextual factors (e.g., acculturation). Analyzing influence of correlated behavioral and sociocultural traits can better identify targets for future intervention. The current study aimed to account for interdependent risk and resilience factors associated with cognitive decline in non-demented older U.S.-dwelling Chinese adults. METHODS The sample consisted of 1,528 older U.S. Chinese (60 and older) with normal cognition and function at baseline who attended three waves of the Population Study of ChINese Elderly (PINE). We used principal component analysis to generate memory and executive functioning outcomes; factor analysis to reduce behavioral and sociocultural variables into latent constructs; and linear mixed-effects models to evaluate the impact of these constructs, as well as of demographic and medical factors (e.g., cardiovascular disease), on longitudinal rates of cognitive decline. RESULTS Factor analysis identified three main behavioral/sociocultural constructs: stress internalization, neighborhood/community cohesion, and external social support. Among these, only stress internalization – consisting of greater perceived stress, greater hopelessness, and lower conscientiousness – was associated with longitudinal decline in memory, while none with decline in executive functioning. Neither acculturation nor social engagement was related to decline in memory or executive function, even though participants with greater acculturation or social engagement had better baseline cognitive performance. DISCUSSION Using a psychometrically and statistically robust model, we found that only the factor underlying stress processing, hopelessness, and conscientiousness was associated with rates of memory decline in this older non-demented U.S. Chinese cohort. These maladaptive traits have been linked to the Asian model minority stereotype but all the same potentially modifiable. Future studies examining disparate health outcomes must account for inter-relatedness among behavioral and sociocultural factors to identify root causes.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".