Interplay of Neighborhood and Psychosocial Factors in Predicting Trajectories of Allostatic Load Among Latinx Adults in the United States
Bibliographic record
Abstract
Research highlights the independent roles of neighborhood and psychosocial risk and protective factors for accelerated physiological aging. However, the combined role of neighborhood and psychosocial factors for allostatic load among Latinx adults in the U.S. remains unclear. Informed by the Health Disparities Framework, the study aims are to: (1) examine the direct associations between neighborhood (cohesion and disorder) and psychosocial (loneliness) factors, respectively, and allostatic load trajectories; and (2) determine whether family social support moderates the association between loneliness and allostatic load trajectories. Data for Latinx adults ages ≥50 ( n = 319) are from the Health and Retirement Study (waves 2006–2016). Linear mixed models estimated baseline and rate of change in allostatic load, adjusting for sociodemographics. Loneliness was positively associated with baseline allostatic load. This association persisted when we considered neighborhood factors. Family social support moderated the association between loneliness and allostatic load slope. As neighborhood features, loneliness, and physiological dysregulation are each associated with worse cognitive outcomes, findings underscore the protective role of family social support for physiological dysregulation, thereby promoting cognitive resilience.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".