Age Differences and Longitudinal Change in Exposure to Daily Stressors: Three Waves of Diary Data Across 20 Years
Bibliographic record
Abstract
Abstract Exposure to daily stress is an important risk factor for healthy aging. We examined cross-sectional age-related differences and longitudinal aging-related change in stressor exposure across three waves of the National Study of Daily Experiences (N=2,914, M=51.53 years, SD=13.55, 56.35% Female) spanning 20 years. Exposure to six types of stressors (arguments, avoided arguments, work overloads, home overloads, network stressors, other) were obtained from telephone interviews over 8 consecutive days in waves conducted in ~1996, ~2008, and ~2017. Longitudinal analyses revealed declines in stressor exposure across 20 years (p <.01), driven by declines in arguments, work overloads, and network stressors specifically. Cross-sectional analyses indicated that older individuals reported stressors less frequently (p <.01), driven by decreases in arguments, avoided arguments, work overloads, and home overloads specifically. Rates of longitudinal decline did not depend on age at baseline. Results suggest that aging-related changes and baseline age differences inform daily stress trajectories in mid- and later-life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".