Tailoring dyadic coping strategies to attachment style: Emotion-focused and problem-focused dyadic coping differentially buffer anxiously and avoidantly attached partners
Bibliographic record
Abstract
Attachment insecurity is associated with lower satisfaction and lower felt security in romantic relationships, especially during times of stress such as coping with a global pandemic. Heightened external stressors for couples are associated with poorer relationship quality, but how couples cope with stress together, or their dyadic coping strategies, is associated with the maintenance of relationship satisfaction. In the current study, we followed 184 couples living together during the COVID-19 pandemic to test whether specific coping strategies buffered people higher in attachment anxiety and avoidance from lower satisfaction and felt security in the early weeks and ensuing months of the pandemic. Our findings demonstrate that perceiving more emotion-focused dyadic coping—being affectionate and using intimacy—buffered the negative association between attachment anxiety and relationship satisfaction and felt security, both concurrently and over several months of the pandemic. In addition, problem-focused perceived dyadic coping backfired for people higher in attachment anxiety; they felt less satisfied when they perceived more problem-focused coping—which involves being solution-focused and using instrumental support—in their relationship. In contrast, people higher in attachment avoidance were buffered against lower relationship satisfaction when they perceived more problem-focused dyadic coping and were not buffered by emotion-focused coping. The current findings suggest the importance of tailoring coping strategies to a partner’s attachment style for relationship quality and felt security during times of stress.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".