Intrapersonal and Interpersonal Regulation of Positive Affect: The Moderating Effects of Stressor Intensity and Perceived Controllability
Bibliographic record
Abstract
Abstract The association between positive affect regulation and well-being has often been examined at the intrapersonal level, but positive affect regulation may also occur at the interpersonal level and be shaped by contextual factors. Thus, positive affect regulation strategies, including savoring and dampening, were examined in relation to well-being (i.e. positive affect and life satisfaction) at the intra- and inter-personal levels, while also considering the moderating effects of stressor intensity and perceived controllability. Participants included 281 emerging adults ( M age = 21.29; 71% female-identifying), who responded to a 14-day daily diary. Multilevel analyses indicated that individuals reported lower well-being on days when they engaged more in dampening, but greater well-being on days when they engaged more in emotion-focused and self-focused positive rumination. Co-savoring was also positively associated with well-being, but co-dampening was not. Perceived controllability, but not stressor intensity, moderated the associations of dampening, self-focused positive rumination, and co-savoring with life satisfaction. These findings suggest that regulating positive affect independently and with social others has implications for well-being, and that contextual factors also matter.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".