It takes two to co-ruminate: Examining co-rumination as a dyadic and dynamic system.
Bibliographic record
Abstract
Co-rumination-defined as when individuals perseverate on problems with each other, focus excessively on negative feelings, and cyclically discuss the causes and consequences of problems-is often examined from the perspective of the person seeking support or by assigning one rating of co-rumination to a dyad. This approach muddles how each person contributes to the "co" of co-rumination and may have implications for understanding prior work that has shown associations between co-rumination and intrapersonal and interpersonal well-being. We leveraged state space grids to examine co-rumination as a dyadic and dynamic system, as constituted by the temporal unfolding of each dyad member's self-rated social rumination throughout their discussion. From 2019 to 2020, 85 primarily White and female college-aged close friend dyads engaged in a support discussion. After, friends viewed their recorded discussion and rated their individual contributions to the co-rumination process (i.e., social rumination) every 30 s across the 8 min conversation. Results revealed that the more both dyad members got "stuck" engaging in mutually high social rumination (i.e., co-rumination), the more they perceived each other as responsive, viewed the problem as more solved, and disclosers viewed responders as more supportive. In contrast, when only the person disclosing the problem was stuck in high levels of social rumination, only disclosers rated the problem as more solved, indicating fewer overall benefits. Examining co-rumination dyadically and dynamically can reveal when and for whom co-rumination processes are associated with costs and benefits. (PsycInfo Database Record (c) 2025 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".