RUMINATION IN DYADS DURING TIME OF UNCERTAINTY—DAILY LIFE ASSESSMENTS AFTER THE ONSET OF THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Rumination involves repetitive, self-oriented, negative thinking and is known to be detrimental to psychological well-being and health. However, little is known about the extent to which rumination is associated with well-being and health in close relationship partners in older age. Additionally, the pandemic was a time that was characterized by high stress, making it an important context to study rumination. Using daily diary data from 140 Canadian older adults plus a close other of their choice (59% spouses, M = 66.72 years, SD = 13.01 range: 18-87 years, 88% White, 62% women), this project builds on past evidence examining daily life rumination dynamics and extends it to a dyadic perspective beyond couples. For ten days, both dyad members provided evening ratings of daily rumination and affect quality. Multilevel models replicate individual level evidence that higher daily rumination was associated with more daily negative affect and less daily positive affect. Importantly, we also found partner effects such that more close others’ rumination was associated with elevated actors’ negative affect (b = 0.03, p = .038) and reduced actors’ positive affect (b = -0.04, p = .023), suggesting that it is not only one’s own rumination that relates to daily well-being, but also that of a close tie. Findings demonstrate the utility of taking a dyadic perspective on what is typically conceived as an individual-level phenomenon.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".