Effects of Chronic Burden Across Multiple Domains and Experiences of Daily Stressors on Negative Affect
Bibliographic record
Abstract
BACKGROUND: Exposure to chronic psychological stress across multiple life domains (multi-domain chronic burden) is associated with poor health. This may be because multi-domain chronic burden influences daily-level emotional processes, though this hypothesis has not been thoroughly tested. PURPOSE: The current study tested whether (a) multi-domain chronic burden is associated with greater exposure to daily stressors and (b) multi-domain chronic burden compounds negative affect on days with stressors compared to stressor-free days. METHODS: The MIDUS Study (Wave II) and the National Study of Daily Experiences sub-study were conducted from 2004 to 2006 (N = 2,022). Participants reported on eight life domains of psychological stress used to create a multi-domain chronic burden summary score. For eight consecutive days, participants reported the daily occurrence of stressful events and daily negative affect. RESULTS: Participants with greater multi-domain chronic burden were significantly more likely to report daily stressors. There was also a significant interaction between multi-domain chronic burden and daily stressors on negative affect: participants with higher multi-domain chronic burden had greater negative affect on stressor days than stressor-free days compared to those with lower multi-domain chronic burden. CONCLUSION: Participants with higher multi-domain chronic burden were more likely to report daily stressors and there was a compounding effect of multi-domain chronic burden and daily stressors on negative affect. These results suggest that experiencing a greater amount of psychological stress across multiple life domains may make daily stressors more toxic for daily affect.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".