Advancing Research on Psychosocial Stress and Aging with the Health and Retirement Study: Looking Back to Launch the Field Forward
Bibliographic record
Abstract
OBJECTIVES: The Health and Retirement Study (HRS) was designed as an interdisciplinary study with a strong focus on health, retirement, and socioeconomic environment, to study their dynamic relationships over time in a sample of mid-life adults. The study includes validated self-report measures and individual items that capture the experiences of stressful events (stressor exposures) and subjective assessments of stress (perceived stress) within specific life domains. METHODS: This article reviews and catalogs the peer-reviewed publications that have used the HRS to examine associations between psychosocial stress measures and psychological, physical health, and economic outcomes. RESULTS: We describe the research to date using HRS measures of the following stress types: traumatic and life events, childhood adversity, caregiving and other chronic stressors, discrimination, social strain and loneliness, work stress, and neighborhood disorder. We highlight how to take further advantage of the longitudinal study to test complex biopsychosocial models of healthy aging. DISCUSSION: The HRS provides one of the most comprehensive assessments of psychosocial stress in existing population-based studies and offers the potential for a deeper understanding of how psychosocial factors are related to healthy aging trajectories. The next generation of research examining stress and trajectories of aging in the HRS should test complex longitudinal and mediational relationships, include contextual factors in analyses, and include more collaboration between psychologists and population health researchers.
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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 | 0.039 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".