The Effect of Long-Term Changes in Daily Stress Processes on Prospective Health: An Application of Three-Level SEM
Bibliographic record
Abstract
Abstract The study of change over time, contexts, cohorts, and people is influenced by the sampling of observations within longitudinal studies. Intensive measurement designs, embedded within long-term longitudinal studies, provide new opportunities to understand changes in dynamic processes, as well as determinants and consequences of these changes over time. The present investigation examined whether short-term dynamic associations accounted for individual differences in prospective health functioning. We used measurement burst data from the National Study of Daily Experiences subsample (N = 2485) embedded within the Midlife in the United States longitudinal study. Two measurement bursts were separated by ten years, with each containing daily measures of stress and affect across eight consecutive days. Functional health was measured by basic and instrumental activities of daily living at three measurement waves spanning 20 years. Three-level structural equation models were fit to simultaneously model short-term within-person associations between stress and affect (i.e., stress reactivity) and long-term changes in these associations over the ten year period. Individual differences in long-term changes of the short-term dynamic association predicted both basic and instrumental activities of daily living at 20 year follow-up (estimate = 5.26, SE = 2.54, p < .01; and estimate = 5.48, SE = 2.81, p < .01, respectively). These effects were present after adjusting for mean levels of both stress and affect. We highlight how characterizing individuals based on the strength of their within-person associations across multiple time scales can be informative in predicting distal health outcomes.
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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 | 0.043 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".