May I Help You? The Relationship Between Interpersonal Emotion Regulation and Emotional and Relational Wellbeing in Daily Life
Bibliographic record
Abstract
People often get support from others in regulating their emotions, a phenomenon known as interpersonal emotion regulation (IER). However, the relative effectiveness of specific IER strategies for improving emotional and relational wellbeing in daily life is unclear. Here, we report two preregistered, ecological momentary assessment studies, in which we examined how the use of six key IER strategies relates to emotional and relational wellbeing among romantic couples in daily life. Study 1 focused on enacted IER as reported by the regulator, whereas Study 2 focused on perceived IER as reported by the regulated partner. Using a dyadic experience sampling design (6 beeps/day for 7 days), Study 1 (N = 136) showed that when people reported to have given advice or encouraged their partner to suppress their emotions, their partners experienced impaired emotional wellbeing. When people reported to have distracted their partner, their partner experienced enhanced positive affect and felt closer to their partner. The use of interpersonal reappraisal, acceptance and ignoring was unrelated to partners’ momentary wellbeing. Using a dyadic daily diary design (1 beep/day for 12 days), Study 2 (N = 361) showed that perceptions of one’s emotions being ignored by the partner were associated with impaired emotional and relational wellbeing on the same day. The perceived use of other IER strategies was unrelated to momentary wellbeing. Taken together, the present set of studies illuminates how IER processes shape people’s emotions and relationships in ecologically valid settings. Our findings indicate that enacted and perceived regulatory behaviors are associated with differential outcomes, highlighting the complex nature of interpersonal emotion dynamics.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".