Combined effects of cumulative stress and daily stressors on daily health.
Bibliographic record
Abstract
OBJECTIVE: It has been proposed that cumulative stress, one's experience of chronic stressors across multiple domains, worsens health by altering the extent to which daily stressors impact daily affect and physical symptoms. Recent work confirms that high cumulative stress exacerbates the association between daily stressor exposure and increased daily negative affect, though it remains untested the extent to which cumulative stress and daily stressor exposure interact to predict daily symptoms. METHOD: = 56.2; 57.2% female) to examine whether levels of cumulative stress compound daily symptoms on days with (vs. without) stressful events. Experiences of life stressors across eight domains, occurrence of daily stressors, and occurrence, number, and severity of daily physical symptoms were analyzed using multilevel modeling. RESULTS: Greater cumulative stress and experiencing (vs. not experiencing) a daily stressor independently increased the odds of occurrence, number, and severity of daily symptoms (ps ≤ .016). Moreover, after adjusting for covariates (e.g., sociodemographic characteristics, chronic health conditions, percent of days with reported stressors, and health behaviors), the associations between daily stressor exposure and odds of occurrence, number, and severity of daily symptoms were potentiated as levels of cumulative stress increased (ps ≤ .009). CONCLUSIONS: The negative implications of daily stressor exposure for daily health may be most pronounced in those who report higher levels of cumulative stress across multiple life domains and across time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".